Spectral analysis and machine learning for determining cluster efficiency during fracking operations

ABSTRACT

This disclosure presents systems, methods, and apparatus for determining cluster efficiency during hydraulic fracturing, the method comprising: measuring acoustic vibrations in fracking fluid in a fracking wellhead, circulating fluid line, or standpipe of a well; converting the acoustic vibrations into an electrical signal in a time domain; recording the electrical signal to memory; analyzing the electrical signal in the time domain for a window of time and identifying two amplitude peaks corresponding to a fracture initiation; measuring a time between the two amplitude peaks; dividing the time by two to give a result; multiplying the result by a speed of sound in the fracking fluid to give a distance between the fracture initiation and a plug at an end of a current fracking stage of the well; and returning a location of the fracture initiation to an operator based on the distance between the fracture initiation and the plug.

CLAIM OF PRIORITY UNDER 35 U.S.C. § 119

The present application is a continuation of U.S. patent applicationSer. No. 17/782,125 filed Jun. 2, 2022 and entitled “SPECTRAL ANALYSISAND MACHINE LEARNING FOR DETERMINING CLUSTER EFFICIENCY DURING FRACKINGOPERATIONS” which is a National Phase Application based onPCT/US20/64327 filed Dec. 10, 2020 entitled “SPECTRAL ANALYSIS ANDMACHINE LEARNING FOR DETERMINING CLUSTER EFFICIENCY DURING FRACKINGOPERATIONS” which claims priority to U.S. Provisional Application Nos.62/945,929 filed Dec. 10, 2019, 62/945,949 filed Dec. 10, 2019,62/945,953 filed Dec. 10, 2019, 63/058,534 filed Jul. 30, 2020,63/058,548 filed Jul. 30, 2020 and 62/945,957 filed Dec. 10, 2019entitled “Spectral Analysis and Machine Learning to Detect Offset WellCommunication Using High Frequency Acoustic or Vibration Sensing”,“Acoustic and Vibrational Sensor Based Micro-Seismic Analysis”,“Spectral Analysis and Machine Learning of Well Activity Using HighFrequency Pressure Sensing of Phase-Locked Stimulation”, “SpectralAnalysis and Machine Learning of Acoustic Signature of WirelineSticking”, “Spectral Analysis, Machine Learning, and Frac ScoreAssignment to Acoustic Signatures of Fracking Events”, and “SpectralAnalysis and Machine Learning of Acoustic Signature of Drill BitPositive Displacement Motor Torque and Drill Bit Wear”, respectively,each of which are assigned to the assignee hereof and hereby expresslyincorporated by reference herein.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to oil field monitoring. Inparticular, but not by way of limitation, the present disclosure relatesto systems, methods and apparatuses for determining cluster efficiencyduring hydraulic fracturing operations.

DESCRIPTION OF RELATED ART

Unconventional reservoirs include reservoirs such as tight-gas sands,gas and oil shales, coalbed methane, heavy oil and tar sands, andgas-hydrate deposits. These reservoirs have little to no porosity, thusthe hydrocarbons may be trapped within fractures and pore spaces of theformation. Additionally, the hydrocarbons may be adsorbed onto organicmaterial, for instance, of a shale formation. In some cases, thesereservoirs may require special recovery operations distinct fromconventional operating practices in order to mobilize and extract theoil.

The rapid development of extracting hydrocarbons from theseunconventional reservoirs can be tied to the combination of horizontaldrilling and induced fracturing (also called “hydraulic fracturing” orsimply “fracking”) of the formations. Hydraulic fracturing operationsmay include at least drilling of a well or borehole into thesubterranean formation, perforation gun (or perf gun) firing, frac fluidpumping, proppant pumping, and plug installation. Horizontal drillinghas allowed for drilling along and within hydrocarbon reservoirs of aformation to capture the hydrocarbons trapped within the reservoirs. Insome cases, an amount of mobilization may be related to the number offractures in the formation, the size of fractures, and evenness ofdistribution of fractures throughout a stage.

SUMMARY

The following presents a simplified summary relating to one or moreaspects and/or embodiments disclosed herein. As such, the followingsummary should not be considered an extensive overview relating to allcontemplated aspects and/or embodiments, nor should the followingsummary be regarded to identify key or critical elements relating to allcontemplated aspects and/or embodiments or to delineate the scopeassociated with any particular aspect and/or embodiment. Accordingly,the following summary has the sole purpose to present certain conceptsrelating to one or more aspects and/or embodiments relating to themechanisms disclosed herein in a simplified form to precede the detaileddescription presented below.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the disclosure. Asused in the specification and in the claims, the singular form of ‘a’,‘an’, and ‘the’ include plural referents unless the context clearlydictates otherwise.

Aspects of the present disclosure generally relate to analyzing highfrequency acoustic or vibration signals in a well to assess fracturedistribution in real-time via time domain and/or spectral analysis ofsaid signals. This technique is far superior to static or absolutepressure readings that have long been used to obtain much lowerresolution and non-real time insights into subterranean activity. Insome cases, the analyzed signals may be transmitted from the lowerreaches of the well to the surface, for instance, through liquid in thewell. This may not only allow real-time feedback during well operationsbut may also allow computer algorithms to determine cluster efficiencyduring hydraulic fracturing operations by comparing the feedback tofeedback previously received as a result of certain fracking operationsor subterranean activity (e.g., fracture initiation or high rate offluid flow through a propped fracture). In some cases, by identifyingmultiple amplitude peaks corresponding to a fracture initiation (e.g.,first and second amplitude peaks corresponding to arrival times of aprimary wave and a reflected wave, respectively, generated by thefracture initiation), one or more of a distance from the fractureinitiation to a plug and a location of the fracture initiation may beobtained. In this way, the distance from the fracture initiation to theplug may be used to create a cluster map of fracture initiations in thestage of a well, which may in turn be used to adjust well operations tomore evenly disperse fractures within fracture clusters. In some cases,the adjustment may be automatic or manual (e.g., based on input from ahuman operator), and may facilitate optimizing well output/yield. Theamount of dispersion of fractures within a stage is referred tothroughout this disclosure as “cluster efficiency.” Various distributiontheories can be used to quantitatively score cluster efficiency, butgenerally, when different clusters of fractures are evenly spaced fromeach other and each of those clusters has a similar number of fractures,then cluster efficiency is greatest (or closest to 1 on a scale of 0-1scale).

Some embodiments of the disclosure may relate to a system fordetermining cluster efficiency during hydraulic fracturing operations,the system comprising: a sensor coupled to a fracking wellhead,circulating fluid line, or standpipe of a well and configured to convertacoustic vibrations measured in fracking fluid in the fracking wellhead,circulating fluid line, or standpipe into an electrical signal in a timedomain; a memory configured to store the electrical signal; amachine-learning system trained on previous electrical signals measuredas a result of previous fracture initiations, the machine-learningsystem comprising tangible non-transient computer readable media encodedwith processor reasonable instructions that when executed cause themachine-learning system to: analyze the electrical signal in the timedomain for a window of time and identify two amplitude peakscorresponding to the a fracture initiation, a first of the two amplitudepeaks corresponding to an arrival time of a primary wave generated bythe fracture initiation, a second of the two amplitude peakscorresponding to an arrival time of a reflected wave generated by thefracture initiation and reflected off a plug at an end of a currentfracking stage of the well; measure a time between the two amplitudepeaks; divide the time by two to give a result; multiply the result by aspeed of sound in the fracking fluid thought to be near the fractureinitiation to give a distance from the fracture initiation to the plug;and a user interface configured to return a location of the fractureinitiation to an operator based on the distance between the fractureinitiation and the plug. In other embodiments, not just a location of asingle fracture, but a map or score showing cluster efficiency for aplurality of fractures within one or more stages can be provided.

Some embodiments of the disclosure may relate to a system fordetermining cluster efficiency during hydraulic fracturing operations,the system comprising: a sensor coupled to a fracking wellhead,circulating fluid line, or standpipe of a well and configured to convertacoustic vibrations measured in fracking fluid in the fracking wellhead,circulating fluid line, or standpipe into an electrical signal in a timedomain; a memory configured to store the electrical signal; amachine-learning system trained on previous electrical signals measuredas a result of previous fracture initiations, the machine-learningsystem comprising tangible non-transient computer readable media encodedwith processor reasonable instructions that when executed cause themachine-learning system to: analyze the electrical signal in the timedomain for a window of time and identify two amplitude peakscorresponding to a fracture initiation, a first of the two amplitudepeaks corresponding to an arrival time of a primary wave generated bythe fracture initiation, a second of the two amplitude peakscorresponding to an arrival time of a reflected wave generated by thefracture initiation and reflected off a plug at an end of a currentfracking stage of the well; measure a time between the two amplitudepeaks; divide the time by two to give a result; multiply the result by aspeed of sound in the fracking fluid to give a distance from thefracture initiation to the plug; and a user interface configured toreturn a location of the fracture initiation to an operator based on thedistance between the fracture initiation and the plug.

Some other embodiments of the disclosure may relate to a method ofdetermining cluster efficiency during hydraulic fracturing operations,the method comprising: measuring acoustic vibrations in fracking fluidin a fracking wellhead, circulating fluid line, or standpipe of a well;converting the acoustic vibrations into an electrical signal in a timedomain; recording the electrical signal to a memory; analyzing theelectrical signal in the time domain for a window of time andidentifying two amplitude peaks corresponding to a fracture initiation;measuring a time between the two amplitude peaks; dividing the time bytwo to give a result; multiplying the result by a speed of sound in thefracking fluid to give a distance between the fracture initiation and aplug at an end of a current fracking stage of the well; and returning alocation of the fracture initiation to an operator based on the distancebetween the fracture initiation and the plug.

Some other embodiments of the disclosure may relate to a method of moreevenly dispersing fractures within fracture clusters during hydraulicfracturing operations, the method comprising: pumping fracking fluidinto a stage of a well; measuring acoustic vibrations in fracking fluidin a wellhead, circulating fluid line, or standpipe of the well;converting the acoustic vibrations into an electrical signal in a timedomain; recording the electrical signal to a memory; identifying afracture initiation from the electrical signal in the time domain viaidentification of two amplitude peaks occurring within a thresholdperiod of time of each other; measuring a time between the two amplitudepeaks; dividing the time by two to give a result; multiplying the resultby a speed of sound in the fracking fluid to give a distance between thefracture initiation and a plug at an end of a current fracking stage ofthe well; using the distance to create a cluster map of fractureinitiations in the stage of the well; and adjusting a parameter of thehydraulic fracturing operations based on the cluster map to achieve moreeven dispersion of fractures in a subsequent stage.

Yet other embodiments of the disclosure may relate to a non-transitory,tangible computer readable storage medium, encoded with processorreadable instructions to perform a method of more evenly dispersingfractures within fracture clusters during hydraulic fracturingoperations, the method comprising: pumping fracking fluid into a stageof a well; measuring acoustic vibrations in fracking fluid in awellhead, circulating fluid line, or standpipe of the well; convertingthe acoustic vibrations into an electrical signal in a time domain;recording the electrical signal to a memory; identifying a fractureinitiation from a current frequency domain spectrum via amachine-learning system trained on previous frequency domain spectrameasured as a result of previous fracture initiations and previouslyclassified by the machine-learning system; analyzing the electricalsignal in the time domain during a window of time and identifying twoamplitude peaks corresponding to the fracture initiation found in thecurrent frequency domain spectrum; measuring a time between the twoamplitude peaks; dividing the time by two to give a result; multiplyingthe result by a speed of sound in fracking fluid thought to be near thefracture initiation to give a distance from the fracture initiation to aplug; using the distance to create a cluster map of fracture initiationsin the stage of the well; and adjusting a parameter of the hydraulicfracturing operations based on the cluster map to achieve more evendispersion of fractures in a subsequent stage.

Yet other embodiments of the disclosure may relate to a non-transitory,tangible computer readable storage medium, encoded with processorreadable instructions to perform a method for determining clusterefficiency during hydraulic fracturing operations, the methodcomprising: converting the acoustic vibrations into an electrical signalin a time domain; recording the electrical signal to a memory;identifying a fracture initiation from the electrical signal in the timedomain via identification of two amplitude peaks occurring within athreshold period of time of each other; measuring a time between the twoamplitude peaks; dividing the time by two to give a result; multiplyingthe result by a speed of sound in the fracking fluid to give a distancebetween the fracture initiation and a plug at an end of a currentfracking stage of the well; and using the distance to create a clustermap of fracture initiations in the stage of the well.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects and advantages and a more complete understanding of thepresent disclosure are apparent and more readily appreciated byreferring to the following detailed description and to the appendedclaims when taken in conjunction with the accompanying drawings:

FIG. 1 illustrates a drilling system for monitoring acoustic orvibration signals in an observation well according to an embodiment ofthe disclosure.

FIG. 2 illustrates a drilling system for monitoring acoustic orvibration signals in an observation well according to an embodiment ofthe disclosure.

FIG. 3 illustrates a network structure for multiple spoke computersaccording to an embodiment of the disclosure.

FIG. 4 illustrates a system for using frequency signatures of acousticor vibration data to determine cluster efficiency according to anembodiment of the disclosure.

FIG. 5 illustrates a well head embodying the herein-disclosed acousticsensor in accordance with one or more implementations.

FIG. 6 illustrates an isometric view of four offset wells, eachincluding a vertical and horizontal region separated by the heel, inaccordance with one or more implementations and a first path of acousticor vibration waves.

FIG. 7 illustrates the isometric view of the four offset wells of FIG. 6and a second path of acoustic or vibration waves.

FIG. 8 illustrates the isometric view of the four offset wells of FIG. 6and a third path of acoustic or vibration waves.

FIG. 9 illustrates the isometric view of the four offset wells of FIG. 6and a fourth path of acoustic or vibration waves.

FIG. 10 illustrate the isometric view of the four offset wells of FIG. 6, but with a different fracture event and a first path of acoustic orvibration waves from the different fracture event.

FIG. 11 illustrates the isometric view of the four offset wells of FIG.10 and a second path of acoustic or vibration waves.

FIG. 12 illustrates the isometric view of the four offset wells of FIG.10 and a third path of acoustic or vibration waves

FIG. 13 illustrates the isometric view of the four offset wells of FIG.10 and a fourth path of acoustic or vibration waves

FIG. 14 illustrates a first exemplary spectral plot with frequencyspikes associated with frac initiation in an observation well, inaccordance with one or more implementations.

FIG. 15 illustrates a second exemplary spectral plot with frequencyspikes associated with frac initiation in an observation well, inaccordance with one or more implementations.

FIG. 16 illustrates a third exemplary spectral plot with frequencyspikes associated with frac initiation in an observation well, inaccordance with one or more implementations.

FIG. 17 shows an embodiment of a method for determining clusterefficiency, and optionally controlling fracking operations to achievemore even fracture dispersion, according to an embodiment of thedisclosure.

FIG. 18 illustrates a computing system configured for determiningcluster efficiency, and optionally controlling fracking operations toachieve more even fracture dispersion, in accordance with one or moreimplementations.

FIG. 19 illustrates an exemplary well including an acoustic or vibrationsensor at the wellhead according to an embodiment of the disclosure.

FIG. 20 illustrates a drilling system for determining clusterefficiency, and optionally controlling fracking operations, according toan embodiment of the disclosure.

FIG. 21 illustrates a method of measuring and/or improving clusterefficiency.

DETAILED DESCRIPTION

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

Preliminary note: the flowcharts and block diagrams in the followingFigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, some blocks in these flowcharts or block diagrams mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The present disclosure relates generally to oil field monitoring. Inparticular, but not by way of limitation, the present disclosure relatesto systems, methods and apparatuses for time domain as well as spectralanalysis of acoustic, vibration, and optionally pressure signalsreceived at a well head to quantify cluster efficiency.

Acoustic and Vibration Pressure

Existing pressure sensing techniques for oil field monitoring involverecording pressure changes (e.g., absolute changes over long periods oftime) with reference to an absolute pressure of fluid in the well.However, currently used static pressure sensors usually have a slowsample rate (e.g., slower than 1 Hz) and are incapable of sensingfracture initiations. However, analyzing relative fluctuations orvibrations in the fluid in a well, for instance, in both a frequencydomain and a time domain may serve to provide a more accurateunderstanding of cluster efficiency, fracture dispersion, potentialwashout (i.e., is water or fluid going toward one crack because it issofter than others) and/or screen out situations, to name a fewnon-limiting examples, than is possible using traditional staticpressure sensors. In some cases, determining cluster efficiency mayprovide insight on fracture dispersion (i.e., how evenly distributed arefractures throughout a stage) within fracture clusters of a stage. Aneven distribution may enhance oil production from a given well. Intypical cases, fracture dispersion is not even (i.e., low clusterefficiency), which adversely impacts production. However, according toaspects of this disclosure, real-time analysis of acoustic vibrations atthe wellhead together with adjustment of one or more parameters of thehydraulic fracturing operations may allow a more even dispersion offractures, thus optimizing efficiency and oil production.

It has been found that analyzing rapid fluctuations or vibrations influid in a well (e.g., fracking fluid) in a frequency domain, ratherthan a time domain, or in combination with time domain analysis, mayprovide a more accurate understanding of fracture locations. In somecases, this spectrum may also be referred to as an acoustic or vibrationspectral frequency signature (or a frequency signature). In someembodiments, the analysis may comprise generating machine learning (ML)models, or other artificial intelligence (AI) models, and training themodels to recognize the acoustic or vibration signatures of differentevents, especially fracture initiations. By training on differentevents, such as rushing water compared to a fracture initiation, themodel can more accurately distinguish fracture initiations from otherevents. Once models have been trained to recognize the acoustic orvibration signatures of different events, real-time acquired data may becompared to the model or analyzed by the model for real-time assessmentof a number, rate of creation, location, dispersion, and quality offractures being created in a given stage, which may be used to adjustfracking parameters such as stage timeline, pH, fracking fluid pressure,perforation gun pressure, distance from peroration gun to plug prior tofiring, etc. In some examples, the real-time acquired data may also beused to differentiate between actual crack openings and mere horizontalshifts in the subterranean formation.

In contrast, known well-monitoring techniques often involve operatorsmaking decisions based on geological modeling performed hours before anoperating step or using trial and error to make informed decisionsbefore a particular step. However, the systems and methods disclosedherein alleviate some of the deficiencies of current techniques byutilizing real-time quantitative and qualitative analysis of crackformation and fluid flow via acoustic and vibration data analyzed in thetime domain and optionally also in the frequency domain, to moreaccurately assess the choices that operators make to achieve highercluster efficiency, and to optionally provide automated and optimizedcontrol of fracking operations to achieve higher cluster efficiency.

In some embodiments, the cluster efficiency may be determined byanalyzing the fluctuations or vibrations in the fracking fluid in awellhead, circulating fluid line, or standpipe of the well (i.e., viameasurement of acoustics in the fluid), or alternatively, by feelingvibrations through a metal component of the wellhead. In some cases,this analysis may involve acquiring dynamic acoustic or vibration datafrom the well's fluid (e.g., fracking fluid) in a time domain, andoptionally converting it into a frequency spectrum or frequency domain.In some circumstances, the analysis can focus on identifying distinctamplitude peaks in the time domain corresponding to a fractureinitiation, wherein a first amplitude peak may correspond to an arrivaltime of a primary wave generated by the fracture initiation, while asecond amplitude peak may correspond to an arrival time of a reflectedwave (e.g., a wave reflected off a plug at an end of a current frackingstage of the well) generated by the fracture initiation. Using thefrequency domain may provide further accuracy to distinguish these peaksfrom noise that might otherwise dominate in the time domain.

Acoustic and Vibration Sensors

In some cases, the techniques described in this disclosure may utilize ahigh frequency (e.g., greater than 1 kHz) acoustic or vibration sensordirectly coupled to a well, for instance at the well head, circulatingfluid line, or standpipe. This acoustic or vibration sensor may be indirect physical contact with fluid in the well, the well casing, thewell head pipe, the circulating fluid line, the standpipe, or the wellpad (e.g., vibration sensors on the well pad can obviate the need tohave direct contact with fluid in the well), to name a few non-limitingexamples of structural connections allowing acoustic and vibrationsensing of well fluid. In some embodiments, a vibration sensor need notbe directly coupled to a component of the well, but instead canindirectly measure vibrations in the fluid. For instance, a laserreflecting off a surface of the well, such as a viewing window, couldmeasure vibrations of the viewing window using optical methods. For thesake of brevity, an acoustic or vibration sensor may be used whereverthe term acoustic sensor is seen in this disclosure. In some examples,the high frequency acoustic or vibration sensor may provide a digital oranalog signal indicative of high frequency pressure fluctuations.Additionally or alternatively, the signal may be indicative ofvibrations in the fluid. In some examples, this signal may be passed toa conversion and analysis component, or a converter (e.g., spectrumanalyzer), configured to identify frequency components of the signal(e.g., via an algorithm that transforms pressure or vibration data inthe time domain to the frequency domain such as a Fast Fourier Transform(FFT) and compares the frequency domain signal to previously measuredfrequency domain signals or signatures). FIGS. 14-16 show examples offrequency spectra that were used to identify subterranean events such asfracture initiation or extension much sooner than is possible withtraditional techniques, or using fewer sensors (e.g., one).

In some examples, the acoustic sensors described throughout thisdisclosure may or may not have a reference pressure. Furthermore, theacoustic sensors may be configured to measure at least changes inpressure. Thus, in some cases, the acoustic sensors may be configured tomeasure both absolute pressure and dynamic pressure (i.e., if areference pressure is being used). Additionally or alternatively, forinstance, if no reference pressure is being used, the acoustic sensorsmay be used in parallel with a separate absolute pressure sensor. Insome cases, the absolute pressure sensor may be configured to measurestatic or absolute pressures, where the absolute pressure may be used asa baseline (or reference) for the higher sensitivity data from theacoustic sensor looking at dynamic pressure.

It should be noted that throughout this disclosure, a vibration sensormay be used in addition (or as an alternative) to a high frequencyacoustic pressure sensor.

Acquiring Data

As previously noted, the term acoustic sensor may be broadly used torefer to a high frequency acoustic pressure sensor and/or a vibrationsensor (e.g., sampling at ˜1 kHz or greater). One non-limiting exampleof a vibration sensor may comprise a piezoelectric vibration sensor. Insome cases, piezoelectric vibration sensors may be configured togenerate a current or voltage proportional to an amount of piezoelectricmaterial movement. The piezoelectric material can be in direct physicalcontact with the fluid in the well or may be physically coupled to aprotective membrane that is in direct physical contact with the fluid.Either way, vibrations in the fluid may be transmitted to thepiezoelectric material, which may cause movement or vibrations of thepiezoelectric material. Movement of the piezoelectric material maygenerate a current or voltage, where the current or voltage may beproportional to the amount of vibration or movement of the piezoelectricmaterial. The ICP Pressure Sensor, Model Number 113B23, is onenon-limiting example of an acoustic or vibration sensor.

In some cases, the generated current or voltage may be recorded andstored, and there may be a 1 to 1 mapping of vibration data to currentor voltage data. The measured current or voltage readings may be used todetermine vibration data, for instance, by mapping the current orvoltage readings to corresponding vibration values in a look-up table.In some cases, raw data may comprise one or more of the mapped vibrationdata, and the measured current and/or voltage readings. This raw datacan be passed through a transform operation such as a Fourier Transform,and further analyzed in the frequency domain (e.g., via a spectrumanalyzer), further described below.

Analysis of Acquired Data and Example Applications/Use Cases

Raw Time Domain and Frequency Domain Analysis

In some embodiments, underground events such as fracking fluid movementor fracture initiation can be the source of acoustic signals, andanalysis can look both at the frequency spectra as well as time domaindata. By using a combination of analysis methods, more accurateidentification of subterranean formations and events may be possible,further described below.

In some examples, a transform component and a conversion and analysiscomponent (e.g., converter or spectrum analyzer) may be implemented as asoftware program, firmware module, hardware comprising analog circuits,or a combination thereof. In some embodiments, a conversion function(e.g., Fourier Transform) may comprise the use of wavelet analysistechniques. In some cases, wavelet analysis may refer to the use of acustom function that is stretched and scaled. In some aspects, waveletanalysis may facilitate in optimizing analysis of detailed timing ofevents in a signal.

For the purposes of this disclosure, a conversion and analysis component(e.g., spectrum analyzer) may be configured to measure the magnitude ofan input signal at different frequencies. Said another way, theconversion and analysis component may analyze signals in the frequencydomain, rather than the time domain. Typically, the conversion andanalysis component may receive electrical signals as an input. In someother cases, the conversion and analysis component may receive acousticor vibration signals via an appropriate transducer. In some embodiments,the conversion and analysis component may utilize a Fourier Transform oranother applicable transform algorithm to convert raw acoustic orvibration data from the time domain to the frequency domain.

Fracking pads may include one or more acoustic sensors (e.g., oneacoustic sensor for each well head) or one or more static and one ormore acoustic sensors (e.g., one static and one acoustic sensor for eachwell head). The acoustic sensors may be high frequency pressure sensors(e.g., sampling at ˜1 kHz or greater). Each fracking pad may include atransceiver for transmitting raw data from its sensor(s) to a local orcloud-based conversion and analysis component. Additionally oralternatively, the raw data may be transmitted to a processing resourcethat receives and analyses outputs from various conversion and analysiscomponents. In one embodiment, a set of pads may comprise a mastertransceiver configured to receive data from one or more other pads on alocal network. Each pad can transmit raw data or converted data (i.e.,frequency domain data) to the master transceiver, and the mastertransceiver may transmit (i.e., relay) the data received on the localnetwork to a cloud-based resource, such as a server farm where morecomplex analysis takes place (e.g., comparison to a model; training amodel).

Further, the acoustic sensors may be coupled to one or more conversionand analysis components. In some cases, the number of conversion andanalysis components may vary (e.g., one for each pad, one for each wellhead, or one for a network of sensors, to name a few non-limitingexamples). The conversion and analysis component may be configured toexecute an algorithm, such as a FFT algorithm, for transforming raw datafrom the time domain to the frequency domain. In some other cases, theconversion and analysis component may be used in concert with anotherdevice or software module that can perform FFT.

Using spectral analysis rather than static pressure-based sensingenables higher signal to noise ratios than traditional staticpressure-based sensors. For instance, a fracture initiation or extensionmay cause an acoustic pop or a rapid surge in acoustic energy at acertain frequency. Static (or absolute) pressure may not change or maysee a very subtle changes from this event, the type of pressure changethat is difficult to discern from noise (typical variations instatic/absolute pressure). However, when viewed in the frequencyspectrum, this pop may look like the spectral signatures seen in FIGS.14-16 , sharp and well-defined peaks with large amplitudes as comparedto the spectral noise floor, and thus be easily delineated from steadystate and other downhole events. By training a model based on frequencysignatures of previous fractures, it is possible to associate a currentfrequency spectrum with one caused by a previous fracture initiation,and thus use acoustic measurements to identify fracture initiation. Inother cases, after identifying pops associated with fracture initiationvia an FFT algorithm (or another Discrete Fourier Transform (DFT)algorithm), the distance of a fracture initiation or cluster from a plugmay be obtained to pin-point an origination of the shockwave (i.e.,location of the fracture initiation). Specifically, since the locationof the plug and the speed of sound/acoustic vibrations through thefracking fluid are known, the distance of the fracture initiation may beobtained based on a comparison of the timing of the primary wavegenerated by the fracture initiation with respect to a secondary orreflected wave reflected off the plug. In some other cases, horizontalshifting, an event that does not improve hydrocarbon recovery, but doescause significant vibrations that may look like a fracture totraditional micro-seismic arrays, has a unique frequency signaturecompared to fracture initiation. By comparing a current frequencysignature to known signatures measured during previous horizontal shiftsand fracture initiations or extensions, one can accurately distinguishbetween horizontal shifts and fracture initiation.

In another example, fluid, mud, and proppant flowing through the wellholes, perforations in stage walls, and fractures may also be associatedwith identifiable signatures in the frequency domain. For instance, anunknown/natural fracture may have created a weak spot causing all fluidsto head down a cluster to the natural crack, or alternatively, asubstantially solid area may not be receiving any proppant. In suchcases, frequency domain analysis may be used to identify such areas(i.e., hard or soft regions). Corrective suggestions can be generated oractions taken. In one example, where a soft region or natural crack inthe subterranean formation has been identified, closing up the soft areamay serve to put more pressure on a hard region, allowing a more evendispersion of fractures. In some cases, rock salt or benzoic acid may besent downhole and seal or close up the soft area. Due to their naturalsolubility, the rock salt or benzoic acid may partially plug fracturesin the short run, but dissolve by the time production is started.

In another example, sand moving along edges of a pipe, well hole, orfracture may generate acoustic or vibration waves at a unique frequency(or frequencies) (e.g., at a different frequency or frequencies fromsignals generated by clean water moving through the same structure). Insuch cases, different areas associated with different geological andflow properties may be identifiable based on analyzing signals in thefrequency domain. In such cases, the conversion and analysis componentmay identify frequency signatures associated with specific activitiesand/or arriving from specific locations in the observation or offsetwell, where the identifying may be based at least in part ondistinguishing between different processes or events during developmentof a well. In some cases, the frequency signatures may be dependent onfluid flow properties. As an example, a first area (e.g., where fluidsbecome turbulent) may be associated with a frequency signature that isdistinct from another frequency signature associated with a second area(e.g., associated with laminar fluid flow).

In some cases, sound or pressure waves may reflect off of variousstructures, interfaces between different fluids, etc., within a well. Insuch cases, the conversion and analysis component may be used toidentify beat frequencies or resonances caused by such reflections. Inone example, a pump truck may create a 33 Hz signal (i.e., a firstfrequency signature) during pumping operations. In some embodiments, the33 Hz signal (i.e., frequency signature) may reflect off of one or moresurfaces, including a heel of the well, toe of the well, one or moreother structures at the well or well head, plug and/or perforation gun,etc. In some cases, the reflective bouncing off of the one or moresurfaces (e.g., one or more times per second for a 1-mile deepsurface-to-heel well) may affect the resonant frequency identified bythe conversion and analysis component. For instance, after reflectingand bouncing off of one or more surfaces and/or structures at the wellhead, or a plug, a generated signal, such as the 33 Hz signal, may beidentified as a ˜1 Hz signal (or another frequency different from 33Hz). In other words, the conversion and analysis component may identifya ˜1 Hz signal as the resonant frequency for pump truck signalsreflecting up and down through the vertical segment of a well (eitherthe observation well or an adjacent well). In this way, the resonantfrequency may be used to better understand the structure of a well,including one or more of the length of the borehole, length ofhorizontal sections, length of fractures extending from the horizontalsection, etc.

In some other cases, a time difference between arrival of primary andreflected waves (e.g., reflected off a plug) may be used to extrapolatea location of the fracture initiation. For instance, by dividing thetime difference between amplitude peaks corresponding to a primary waveand a reflected wave by two to get a result, and then multiplying theresult by a speed of a sound in the fracking fluid, a distance from thefracture initiation to the plug may be obtained. Further, a location ofthe fracture initiation may also be determined based on the distancebetween the fracture initiation and the plug since the location of theplug is known. As shown by equation (1), a distance from the plug may beobtained by measuring a direct or primary wave, as well as a secondaryor reflected wave exiting a perforation. It should be noted that, thedirect or primary wave may travel up hole as soon as it exits theperforation, while the secondary wave may first travel downhole, reflectoff of the plug, and then travel up hole. Thus, the primary andsecondary waves may be identified as distinct amplitude peaks in thetime domain data.

$\begin{matrix}{{D = \frac{( {A_{2} - A_{1}} )*S}{2}},} & (1)\end{matrix}$

where D=Distance from Plug; A₁=Arrival Time of Primary Wave; A₂=ArrivalTime of Reflected Wave; and S=Speed of sound in current (fracking)fluid.

In some cases, S may be calculated when a perforating (or perf) gun isfired. For instance, a wave travelling from the perf gun may reflect offof a fluid end (e.g., at the surface), go back down hole, and thenreflect off of the plug. This reflected wave may be measured by thesensor, and its characteristics recorded and analyzed to calculate S.Equation (2) may be employed to calculate S:

$\begin{matrix}{{S = \frac{G + P + {2*( {F + E} )}}{A_{2} - A_{1}}},} & (2)\end{matrix}$

where S=Speed of sound in current fluid; G=Perforating Gun Firing Depth;P=Plug Depth; F=Sensor distance from wellhead; and E=Distance fromSensor to closest fluid end.

In this way, equations (1) and (2) may be utilized to determinelocations of fractures and thus a probable cluster location, as well asa dispersion of fractures within a fracture cluster, and dispersion ofclusters within a stage.

In some examples, resonant frequencies may also be used in assessingdimensions and shapes of underground chambers, such as reservoirs ornatural cracks, or the thickness of different layers of undergroundmaterials, or even the density (i.e., hard or soft region) of aformation surrounding a crack. All of these may present unique frequencyspectra that an acoustic sensor at or near a wellhead can measure andthat can be matched with previous spectra associated with similarstructures.

Machine Learning

Fourier Analysis

Some embodiments of this disclosure pass acoustic or vibration data inthe frequency domain to a machine learning model for analysis, labeling,and training of the model. In some embodiments, the model may beconfigured to use artificial intelligence based on, for example, aneural network or other type of machine learning algorithm. In somecases, the artificial intelligence algorithm or model may receive timedomain data converted to a frequency domain, for instance, using a FFTalgorithm or another algorithm for computing the discrete Fouriertransform (DFT) of a sequence. A DFT may be obtained by decomposing asequence of values into components of different frequencies. In somecases, a conversion and analysis component may be utilized to performthe conversion from time to frequency domain. In some other cases, theacoustic or vibration data in the time domain may be passed to a machinelearning model without conversion. In such cases, the conversion andanalysis component may be responsible for analysis, but not conversion,of the time domain data. It should be noted that, even though noconversion of time domain data into the frequency domain takes place,the model may still have access to frequency information associated withthe measured signal. In some cases, the model may look at a window ofdata in one shot (or one local section of a signal as it changes overtime) and learn to detect, for instance, high and low frequencywaveforms and structures. The model or neural network may encompassknowledge of frequency space decomposition of a signal and may beconfigured to deconstruct a single waveform in time into a composite ofsimpler, underlying waveforms (e.g., sinusoidal waveforms). Thus, insome aspects, the model may be trained to perform something akin toFourier analysis. In some other cases, the model may utilize aShort-time Fourier transform (STFT) to determine the sinusoidalfrequency and phase content of local sections of a signal as it changesover time. STFT computation may involve dividing a longer time signalinto shorter segments of equal length and then computing the Fouriertransform separately on each shorter segment. In some cases, once theFourier spectrum is revealed for each shorter segment, the changingspectra may be plotted as a function of time (i.e., also known as aspectrogram or waterfall plot).

Example Machine Learning Algorithms

In some embodiments, a plurality of distinct machine-learning algorithmsmay be operated in parallel. In some aspects, the use of multiplemachine-learning algorithms may also decrease incorrect identificationsof fracture initiation, or other fracture parameters as compared to theuse of a single machine learning algorithm. In some cases, a combinationof three or four machine learning algorithms may be operated inparallel, which may provide a balance of high accuracy versus systemcomplexity. Some non-limiting examples of machine learning algorithmsmay include a neural network, a decision tree, a support vector machine,and Bayesian methods.

Neural Networks

In some cases, a neural network may comprise a plurality of input nodes,where an input node refers to a point within the neural network to whicha parameter (e.g., a drilling parameter) may be provided for furtherprocessing. Further, the neural network may comprise one or more outputnodes, where each output node represents a calculated and/or predictedparameter based on the input data at the input nodes. In some cases, oneor more layers of hidden nodes may lie between the input and outputnodes, where the hidden nodes may be coupled to some or all of the inputnodes and/or the output nodes. Each of the hidden nodes may beconfigured to perform a mathematical function that is determined orlearned during a training phase of the neural network, where themathematical function may be determined based on the data of the inputnodes to which it is coupled. Likewise, the output nodes may performmathematical functions based on data provided from the hidden nodes. Insome embodiments, the neural network may be provided one or moredrilling parameters in real-time, as well as one or more historicalvalues of the drilling parameters based on preprocessing, for instance,by frac initiation software. In other words, the neural network may betrained using historical data from fracking and drilling operationswhere fracture initiation, extension, or horizontal shifting actuallyoccurred. In such cases, the neural network may produce a value at anoutput node based on an input value provided to the input node, wherethe value may be a probability of occurrence of a fracture initiation orextension event, or some other subterranean occurrence. Somenon-limiting examples of drilling parameters may include a valueindicative of fracking fluid pressure; a value indicative of stage time;a value indicative of pH; and a value indicative of perforation spacing.

Decision Trees

With regards to fracking and drilling operations, a decision treemachine learning algorithm may be an example of a predictive modelcomprising a plurality of interior nodes that may be traversed based ona set of input parameters (e.g., fracking parameters, such as frackingfluid pressure, stage length, pH, etc.). In such cases, the predictedvalue (e.g., of fracture initiation, or a value associated with fracdispersion) may be based on arriving at an end node followingtransitioning from node to node, where the transitioning may be based onthe set of input parameters. In such cases, the end node may be dictatedby the input parameters. It should be noted that, in some cases,decision trees may also be referred to as classification or regressiontrees.

Support Vector Machines

In some cases, support vector machines are a class of machine-learningalgorithms that perform classifications of data into groups. Inparticular, support vector machines can be thought of as performingclassification by analysis of the data in a multidimensional space.Training data for support vector machines may be “plotted” or “mapped”into the multidimensional space and classified or grouped spatially. Itshould be noted that the plotting or mapping need not be a true physicalplotting, but a conceptual operation. After the training phase, data tobe analyzed may be plotted or mapped into the multidimensional space.Further, the support vector machine may be configured to determine themost likely classification of the data. In some cases, theclassification of the data to be analyzed may be a “distance”calculation between the spatial location of the data to be analyzed inthe mappings and the “nearest” classification. In one non-limitingexample, the support vector machine may be provided one or more frackingparameters from fracking operations. In this case, the support vectormachine may be configured to plot the data in a multidimensional spaceand classify the data. During actual fracking operations (i.e., whenreal-time fracking parameters are provided to the support vectormachine), the support vector machine may plot a data point under test inthe multidimensional space, and predict a result (i.e., a probability offracture initiation or increased production) based on the spatialposition of the plotted point relative to a spatial delineation (orclassification line) between data with fracture initiation or extensionevents and those without.

Bayesian Methods

In yet other cases, the machine learning algorithm may comprise the useof Bayesian methods. Bayesian methods represent a logically differentview of data and probabilities and may be thought of as testing theplausibility of a hypothesis (e.g., a fracture extension will occur inthe future) based on a previous set of data. In some aspects, Bayesianmethods may be considered non-deterministic since they generally assumethe plausibility of a hypothesis is based on unknown or unknowableunderlying data or assumptions. In some embodiments, a value indicativeof plausibility of a hypothesis may be determined based on the previousdata (e.g., the training data), following which plausibility may betested again in view of new data (i.e., with the fracking parametersapplied). From the evaluation, a plausibility of the truth of thehypothesis may be determined.

FIG. 1 illustrates a drilling system 100 for monitoring acoustic orvibration signals (referred to simply as, signals) in an observationwell. The signals can either be sourced or generated at (or in) theobservation well or an optional offset well. As shown, the drillingsystem 100 can include a well head 102 of the observation well andoptionally an offset well having an offset well head 120. The well head102 of the observation well can include a sensor 104 (e.g., acoustic orvibration sensor) in physical contact with fluid in the observation wellor a component directly in contact with the fluid (e.g., a sensoraffixed to an outside of the standpipe or wellhead). For instance, thesensor 104 can be arranged within the wellhead, a circulating fluidline, or the standpipe, as shown in FIG. 20 . Alternatively, the sensor104 can be arranged at an end of a T-junction that runs roughlyperpendicular to piping of the wellhead, a circulating fluid line, orthe standpipe. Alternatively, the sensor 104 can be arranged within apipe parallel to piping of the wellhead, circulating fluid line, orstandpipe. The sensor 104 can generate a signal and pass said signal toan onsite computer 106, for instance, via an analog-to-digital converter(ADC) 108. The onsite computer 106 may be configured to process signalsfrom one or more wellheads of a pad, or alternatively, from multiplepads. The onsite computer 106 can include a transceiver or antenna 110configured to transmit raw acoustic or vibration data to a conversionand analysis component. As illustrated, the conversion and analysiscomponent may comprise an on-site or cloud-based storage and analysisunit 112. In some examples, the conversion and analysis component may beconfigured to convert the raw acoustic or vibration data from a time toa frequency domain. Further, the conversion and analysis component maybe configured to identify frequency signatures indicative of one or moreevents. In some cases, identification of such events may further triggera communication to an operator computer 114. Some non-limiting examplesof such events may include a fracture initiation, a potentialcommunication between wells, an eminent drill bit failure, wirelinesticking, etc. In some cases, the operator computer 114 may be linked tothe conversion and analysis component via a transceiver 116, and mayfurther include a display 118 for providing visual warnings or othermessages or indicators.

In some cases, the on-site or cloud-based storage and analysis unit 112may include a trained model (e.g., as part of a machine-learning system)based on previous drilling or hydraulic fracturing operations and theirfrequency signatures (and optionally previously classified by themachine-learning system). For instance, the model may have been trainedusing acoustic or vibration data from previous drilling events, forinstance, an event that led to a falloff in production. Additionally oralternatively, the model may have been trained using a cluster mapassociated with distances from prior fracture initiations to a plug. Inthis way, a mapping of fracture initiations in clusters associated withdifferent plug positions may be determined for a fracking stage, forinstance, by referencing the distance from the current fractureinitiation to the plug with other distances and plug positions fromprior fracture initiations. In some embodiments, the on-site orcloud-based storage and analysis unit 112 may be configured to provideautomated feedback control to the well, for example, to reduce fracpressure, close or seal up a soft area to apply more pressure on aharder region, increase or decrease proppant pressure, vary fracturingfrequency (e.g., instead of 1 frac every 2 seconds, adjust to 1 fracevery 3 or 4 seconds), increase or decrease well spacing of future wells(or change a direction of a well to increase spacing between portions ofadjacent wells), vary perforation cluster locations by controlling thewireline coupled to the perf gun, or perform another applicable action.

In some embodiments, the on-site or cloud-based storage and analysis 112may monitor for a signature of pump trucks 122 pumping fluids into theoffset well head 120. In some cases, these pump trucks 122 may operateat around 33 Hz. In such cases, the frequency signature (i.e., at 33 Hz)generated by the pump truck may have a greater amplitude than otherfrequency components generated by the illustrated drilling system 100.

In some cases, sound or pressure waves may reflect off of variousstructures, such as a plug or perf gun, interfaces between differentfluids or materials, etc., within a well or between wells. In suchcases, the conversion and analysis component may be used to identifybeat frequencies or resonances caused by such reflections. In someembodiments, the 33 Hz signal (i.e., a first frequency signature)generated by the pump truck may reflect off of one or more surfaces,including a heel of the well, toe of the well, one or more otherstructures at the well or well head, one or more wireline tools (e.g.,plug, perf gun), etc. In some cases, the reflective bouncing off of theone or more surfaces (e.g., one or more times per second for a 1-miledeep surface-to-heel well) may affect the resonant frequency identifiedby the conversion and analysis component. For instance, after reflectingand bouncing off of one or more surfaces and/or structures at the wellhead, a generated signal, such as the 33 Hz signal, may have a ˜1 Hzbeat frequency signal (or another frequency different from 33 Hz),corresponding to reflections. In other words, the conversion andanalysis component may identify a ˜1 Hz signal as the resonant or beatfrequency for pump truck signals reflecting up and down through thevertical segment of a well (either the observation well or an adjacentwell) in addition to the original 33 Hz signal. In this way, theresonant frequency may be used to better understand the structure of anobservation or offset well, including one or more of the length of theborehole, length of horizontal sections, length and/or number offractures extending from the horizontal section, dispersion orconcentration of fractures, cluster spacing, etc.

FIG. 2 illustrates a drilling system 200 for monitoring acoustic orvibration signals in an observation well. In some examples, the drillingsystem 200 may implement one or more aspects of the figures describedherein, including at least FIG. 1 . As shown, drilling system 200 maycomprise one or more well pads 202 (e.g., well pad 202-a, well pad202-b), one or more spoke computers 208 (e.g., spoke computers 208-a,spoke computer 208-b), antenna systems 212 (e.g., antenna system 212-a,antenna system 212-b), a remote hub 214, and a database 230. While theillustrated embodiment shows two well pads 212, any number of well padsmay be utilized. Each well pad 212 may include one or more well heads(shown as well head 102 in FIG. 1 ), where each well head can include asensor (shown as acoustic sensor 104 in FIG. 1 ) and optionally anabsolute pressure sensor (or static pressure sensor) directly coupled tofluids in the well (e.g., via the wellhead, circulating fluid line, orstandpipe, to name a few non-limiting examples). Alternatively, eachwell may include an acoustic sensor and an optional absolute pressuresensor, and these sensors may not be directly coupled to fluids in thewell via the well head. For instance, an adapter below the well head maybe used to place the sensor(s) in direct communication with fluid in thewell, or the vibration sensor may be coupled to a metal component (e.g.,standpipe) of the well or well head.

The signals can either be sourced at the observation well (e.g.,acoustic waves from a fracture initiation) or an adjacent or offset well(e.g., acoustic waves from a pumping truck). In some embodiments, thesensor(s) may be configured to couple to processors (e.g., Raspberry Pi)located in the spoke computers 208-a and/or 208-b. In some cases, aspoke computer 208 may comprise one or more processors for each well pad202 in electronic communication with the respective spoke computer. Insome embodiments, the one or more processors of the spoke computers 208may be coupled to an antenna system 212. In some cases, the antennasystem 212 may comprise an omnidirectional antenna, although other typesof antennas are contemplated in different embodiments. Each antennasystem 212 may be in communication with a wide area network (WAN), suchas a 4G or 5G network. In another embodiment, the antennas of theantenna system 212 may form a local area wireless network wherein one ofthe antennas may be configured as an interface (e.g., a gateway) betweenthe local area wireless network and a wide area network. In someembodiments, cellular (e.g., multi-beam antennas, sector antennas) orsatellite (e.g., dish) antennas may be deployed for communication with awide area network, to name a few non-limiting examples. Further,omnidirectional or Yagi type antennas, to name two non-limitingexamples, may be utilized for local area network communication.

In some cases, the remote hub 214 may be in communication with theantenna systems 212 and the spoke computers 208. Further, the remote hub214 may be configured to contact an insight program 226 via anApplication Programming Interface (API) 224. In some examples, thiscommunication may involve a local area network or a wide area network.Insight 226 may be configured to store data for a training model in thedatabase 230, as well as to continually train the model using new dataacquired from the acoustic sensors at the well heads. In some cases, thedrilling system 200 may also support a web app 228 to provide one ormore insights, warnings, feedback, and/or instructions to pad operators.In some examples, the web app 228 may be accessible via a user interfacedisplayed on a user device (e.g., laptop, smartphone, tablet, etc.).

In some embodiments, the processors may comprise (or may be coupled to)a conversion and analysis component. In other embodiments, theprocessors may send their data through the network(s) to a centralizedconversion and analysis component. In some cases, the centralizedconversion and analysis component may or may not be located near thewell pads 202. For instance, the centralized conversion and analysiscomponent may be located off-site in some embodiments.

As illustrated, the drilling system 200 may further comprise one or moreadditional components, modules, and/or sub-systems, including, but notlimited to, a Data Acquisition and Control System (DASTrac 216), afracking client 218, a Coiled Tubing (CT) Data Acquisition module 220,and a CT client 222. In some cases, the DASTrac 216 may comprise a dataacquisition and control program for acquiring fracking operations datafrom wellsite process control units and other sensors. Further, DASTrac216 may be configured to display the acquired data from the dataacquisition system in both numeric and graphical form in real time,which may enable operators to change job profiles, scale parameters,advance stages, change stages, and hold stages in response to seeing adetermined cluster efficiency, to name a few non-limiting examples. Insome cases, the CT Data Acquisition module 220 may be configured tomeasure and control technological parameters of coiled tubing unitsduring repair and stimulation operations of oil and gas wells. The CTData Acquisition module 220 may also be configured to record themeasured technological parameters on electronic media, and optionallydisplay and visualize them on an operator's computer display. In somecases, the CT client 222 may be configured to access coiled tubing datafrom the CT Data Acquisition module 220, for instance, directly via theAPI 224. In the oil and gas industry, coiled tubing may refer to a longmetal pipe, usually anywhere between 1 to 3.25 inches in diameter(although other diameters are contemplated in different embodiments),which is supplied spooled on a reel. In some cases, coiled tubing may beused for interventions in oil and gas wells, as production tubing indepleted gas wells, and/or as an alternative to a wireline (i.e., thecoiled tubing may be used to carry out operations similar to awireline). In some embodiments, coiled tubing may be configured toperform open hole drilling and milling operations. Further, due to theirhigh pressure tolerance abilities (e.g., ranging from 55,000 PSI to120,000 PSI), they may also be utilized to fracture a reservoir. In somecases, one or more sensors (not shown) may be coupled to the coiledtubing and sent downhole. The CT Data Acquisition module 220 may collectreal-time downhole measurements from the sensors, where the measurementsmay be used to model the fatigue on the coiled tubing, predict coiledtubing performance, fluid behavior at modeled downhole well conditions,to name a few non-limiting examples. In some cases, the real-timedownhole measurements collected by the CT Data Acquisition module 220may also be used to optimize treatments, for instance, duringinterventions (i.e., when the well is taken offline).

The spoke computers can include memory for storing electrical signals, acurrent frequency domain spectrum, or both, measured by sensors at oneor more well heads, circulating fluid lines, or standpipes at the wellpads 202-a and 202-b. The database 230 can also include memory forstoring electrical signals, a current frequency domain spectrum, orboth, measured by sensors at one or more well heads, circulating fluidlines, or standpipes at the well pads 202-a and 202-b. The database 230can also be configured to store frequency domain spectra measured duringprevious hydraulic fracturing operations. The database 230 can alsoinclude previous classifications or identifications of subterraneanactivities and events associated with the previous frequency domainspectra. This may include a mapping between events or structures (e.g.,a size, location, number, and/or dispersion of fractures) and previousfrequency domain spectra. The database 230 may also store well outcomesassociated with previous frequency domain spectra. For instance, anincrease in well production after a fracking operation that resulted insome subterranean event (believed to be fracture initiation, extensionor widening) that caused a certain previous frequency domain spectra.These outcomes can include well flow rate and fracture intersection withthe wellbore, to name two non-limiting examples.

FIG. 3 illustrates a network structure 300 for multiple spoke computersaccording to an alternate embodiment of the disclosure. As illustrated,the network structure 300 may comprise ‘N’ spoke computers 308, eachincluding some or all the details shown in the spoke computer 308-a. Insome examples, spoke computers 308 may be electronically andcommunicatively coupled to antenna systems 312. Further, each antennasystem 312 may be in communication with a hub 314. Spoke computers 308,antenna systems 312, and hub 314 may be similar or substantially similarto spoke computers 208, antenna systems 212, and remote hub 214,respectively, previously described in relation to FIG. 2 . In someexamples, spoke computer 308-a may be in electronic communication withsensors (e.g., acoustic or vibration sensors) of a well pad (shown aswell pad 202 in FIG. 2 ). As shown, the well pad may comprise one ormore wells (i.e., wells 302-a, 302-b, 302-c, 302-d, and/or 302-e), eachhaving an acoustic or vibration sensor. Further, these sensors may beconfigured to provide raw data (e.g., time domain acoustic or vibrationsignal data) to the spoke computer 308-a. In some embodiments, the spokecomputers may be configured to wirelessly communicate with the hub 314.Furthermore, the hub 314 may be configured to communicate with a widearea network, for instance, via an antenna system 312 (e.g., 312-d). Insome cases, one or more user/operator devices 305, such as user/operatordevices 305-a, 305-b, 305-c, and/or 305 may be in communication with aconfiguration app 328. The configuration app (also referred to as configapp 328) may be in communication with the hub 314 and may be used toassign sensors to particular wells 302 and/or spoke computers 308, forinstance. The config app 328 may also be used for configuring one ormore of the hub 314, the sensors, and the spoke computers 308.

FIG. 4 illustrates a system 400 for using frequency signatures ofacoustic or vibration data to determine cluster efficiency and/or formore evenly dispersing fractures within fracture clusters duringhydraulic fracturing operations. In this illustration, one or moresensors 404 (e.g., sensor 404-a, sensor 404-b) are coupled to pumptrucks 406 (e.g., pump truck 406-a, pump truck 406-b), where the one ormore sensors 404 may be acoustic or vibration sensors. In some cases,the sensors 404 may be similar or substantially similar to the sensors104 previously described in relation to FIG. 1 . The sensors 404 can beconfigured to make direct or indirect measurements of acoustic orvibration waves in fracking fluid, for instance, via connection to awell's wellhead, circulating fluid lines, standpipe, or piping in a pumptruck. In some embodiments, the sensors 404 may be configured to passraw data (e.g., time domain acoustic or vibration signal data) to aspoke computer 408, which may implement one or more aspects of the spokecomputers 208 and/or 308 described in FIGS. 2 and/or 3 , respectively.Following reception of raw sensor data, the spoke computer 408 maytransmit the raw data using wired or wireless communication to a hub414. Alternatively, the spoke computer 408 may be configured to convertthe raw data in the time domain to the frequency domain, for instance,using a FFT algorithm. As shown, the spoke computer 408 may beconfigured to communicate with the hub 414 using antenna system 412-a.Antenna systems 412 may be similar or substantially similar to theantenna systems 212 and 312 described in relation to FIGS. 2 and 3 . Theantenna systems 412 may comprise one or more of omnidirectional, Yagi,dish, and cellular antennas, to name a few non-limiting examples.

After hub 414 receives the data (e.g., raw data, or frequency domainsensor data) from spoke computer 408, it may further relay said data onto a frequency spectral analysis module 401 via antenna system 412-band/or API 424. The API 424 may implement one or more aspects of API 224discussed in relation to FIG. 2 . In some examples, for instance, if thehub 414 receives time domain data from the spoke computer 408, the hub414 may transmit the time domain data to a conversion and analysiscomponent and receive converted frequency domain data, prior to relayingthe frequency domain data to the API 424.

As illustrated, the frequency spectral analysis module 401 may beelectronically and communicatively coupled to a classification andprediction module 402. The classification and prediction module 402 maybe configured to determine if the frequency spectrum of the raw dataaligns with signatures for known subterranean activity, such as fractureinitiation, fracture extension, horizontal shifting, fractureintersection with well-bore, and fracture intersection with anotherfracture, to name a few non-limiting examples. Alignment with frequencysignatures for known sizes, such as diameter and length of a fracturemay also be looked for. If such a classification occurs, then an eventsand notification module 403 may be activated. In some circumstances, theevents and notification module 403 may be configured to issue anindication to an operator of the pump truck 406 or well. Alternativelyor in parallel, the classification and prediction module 402 may beconfigured to analyze raw time series data and determine if this rawdata aligns with known time series signatures. If such a classificationoccurs, the events and notification module 403 may be activated to sendan indicator to an operator.

Additionally or alternatively, the classification and prediction module402 may be configured to analyze raw time series data, calculatefracture distance from the plug, calculate absolute fracture positionbased on a known position of the plug, repeat this for a plurality offractures in a stage, and estimate a level of frac dispersion (i.e., arecracks dispersed or concentrated) and/or washout (i.e., is water goingtoward one crack because it is softer than other crack(s)), or evenpredict screenout, once positions of two or more fractures are known. Asdescribed later in the disclosure, a cluster efficiency may be assignedbased on the estimations and/or predictions by the classification andprediction module 420. Furthermore, as previously described, fluid, mud,and proppant flowing through the well holes, perforations in stagewalls, early screenout, washout, and/or fractures may be associated withidentifiable signatures in the frequency domain and/or raw time domaindata.

FIG. 5 illustrates a wellhead assembly 500 comprising a wellhead 502 andone or more sensors. It should be noted that, the wellhead assembly 500may be one of an observation or an offset wellhead assembly. As shown,the wellhead 502 may include several interfaces for various sensors suchas a casing pressure sensor 505, a static pressure sensor 506, a tubingtemperature sensor 507, an acoustic or vibration sensor 504, etc. Insome embodiments, different sensors may be provided for the tubing andthe casing since these are separate fluid paths. It should be notedthat, the sensors depicted in FIG. 5 are not intended to be limiting,and more or fewer sensors may be utilized in other embodiments. Forinstance, in some examples, a tubing pressure sensor may be provided inaddition to the casing pressure sensor 505. Additionally oralternatively, a casing temperature sensor may be provided along withthe tubing temperature sensor 507. In some embodiments, the hereindisclosed acoustic or vibration sensor 504 may be coupled to one of theinterfaces of the wellhead 502, such that the acoustic or vibrationsensor 504 is in direct physical contact with fluid in the wellhead 502.In some examples, the acoustic or vibration sensor 504 may comprisewireless transmission hardware for supporting communication with a spokecomputer, hub, or wide area network (not shown).

In some cases, micro-fracturing in a well bore may be performed todefine the stress field and fracture system, for instance, to optimizehydraulic fracturing well completion operations. It should be notedthat, micro-fracturing is to be distinguished from fracturing of thebore hole after drilling, which is typically more intense and is done inorder to facilitate the extraction of oil or gas from the well. In otherwords, micro-fracture testing may be performed for acquisition ofinformation concerning the formation and may be used to optimizefracturing. In some cases, the sensors illustrated in FIG. 5 ,especially the pressure sensors, may be used for micro-fracture testing.

In some cases, acoustic data can be supplemented by graphs of pressureversus time and pressure versus pumped volume. These may be generated bya surface computer system (or alternatively, a remote computer system).An operator or user may control the conduct of the test based on thegenerated graphs. For example, the operator may run the pumppressurizing the bore hole until a drop off or leveling out of thepressure becomes evident, indicating that significant fracturing hasoccurred. In some cases, after the pump is shut down, the operator maycontinue to monitor the graph of pressure versus time in the bore hole.Examination of this data, together with surface pressure and flow data,may be used to determine the pressure at which fracture initiationoccurs (i.e., the bore hole pressure below which the fractures willbegin to close), when fractures are propagated (i.e., pressures atdifferent flow rates (fracture conductivity)), and when primary andpotentially later closure pressures are attained (i.e., when the test iscompleted).

In some cases, micro-fracture testing, unlike leak off tests, may beperformed a number of times along the well bore within the producingreservoir. Microfracture testing may be used to study when and howfractures develop as pressure increases (e.g., a slight decrease inpressure may be observed when a fracture occurs when pressure rises toX). Study of microfracture testing graphs may also provide insight onhow cracks propagate, as well as how fluids flow into extending or openfractures (e.g., after the slight drop in pressure from X, constantpressure Y may be observed despite the pump continuing to pump fluidinto an isolated zone of the bore hole). In some cases, thesemicrofracture testing graphs may provide insight on fracture dispersion,cluster efficiency and/or spacing, etc., which may be useful inoptimizing current and subsequent fracturing stages. Furthermore,microfracture testing may also be used to understand the closure offractures as pressure reduces. For instance, when the pump is stopped, adecay in pressure may be observed even though fluid continues to seepinto the formation. However, in some circumstances, the rate of pressuredecay may reduce over time, which is indicative of the closure of thefracture as the pressure reduces. In other words, as the fracturebecomes smaller, the rate of fluid seepage into the formation, andtherefore the rate of pressure decay, decreases. In some cases, afterthe pressure has decayed, the pressurization of the isolated zone may berepeated to gain additional information concerning the pressure(s) atwhich fractures in the formation proximate a zone of the bore hole willopen and close.

As described above, in some embodiments, the cluster efficiency may bedetermined by analyzing the fluctuations or vibrations in the frackingfluid in a wellhead, circulating fluid line, or standpipe of the well(i.e., via measurement of acoustics in the fluid), or alternatively, byfeeling vibrations through a metal component of the wellhead. In somecases, this analysis may involve acquiring dynamic acoustic or vibrationpressure data from the well's fluid (e.g., fracking fluid) in a timedomain, and optionally converting it into a frequency spectrum orfrequency domain. In some circumstances, the analysis can focus onidentifying distinct amplitude peaks corresponding to a fractureinitiation, wherein a first amplitude peak may correspond to an arrivaltime of a primary wave generated by the fracture initiation, while asecond amplitude peak may correspond to an arrival time of a reflectedwave (e.g., a wave reflected off a plug at an end of a current frackingstage of the well) generated by the fracture initiation. In one example,after identifying pops associated with fracture initiation via an FFTalgorithm (or another Discrete Fourier Transform (DFT) algorithm), thedistance of a fracture initiation from a plug may be obtained (e.g.,using equations (1) and (2) described above) to pin-point an originationof the shockwave (i.e., location of the fracture initiation). In somecases, since the location of the plug and the speed of sound/acousticvibrations through the fracking fluid is known, the distance of thefracture initiation may be obtained based on a difference of the timingof the primary wave generated by the fracture initiation and thesecondary or reflected wave reflected off the plug, along with one ormore other known quantities.

FIG. 6 illustrates an isometric view of four offset wells 600 (e.g.,offset wells 600-a, 600-b, 600-c, and 600-d), each including a vertical(e.g., vertical regions 603-a, 603-b, 603-c, and 603-d) and a horizontalregion 604 separated by a heel 606 (e.g., heel 606-a, 606-b, 606-c,606-d). The horizontal region 604 of each offset well 600 connects arespective heel 606 to a toe 601 (e.g., toe 601-a, toe 601-b, toe 601-c,toe 601-d). In this example, an event 602 has occurred near the toe601-b of the second offset well 600-b. An acoustic sensor or vibrationas described earlier may be coupled to a top or wellhead of the firstoffset well 600-a (e.g., a wellhead, circulating fluid line, orstandpipe). In this case, the first offset well 600-a may be referred toas the observation well. In some cases, the acoustic sensor at thewellhead of the first offset well 600-a may detect the event 602 at thesecond well based on detecting sound or vibrations passing through theunderground formation separating the two offset wells 600-a and 600-b.In some cases, the sound or vibrations associated with the event 602 mayalso pass through one or more fractures 616 (e.g., fractures 616-a,616-b) off of one or both wells, reflect off of a plug or perf gun (oranother wireline tool) in the first and/or second offset well 600, etc.Because pressure or sound tend to move more quickly through dense rockthan through fluids in a fracture or in a well, event 602 may bedetected multiple times at the acoustic sensor. In some cases, thepressure or sound measured by the acoustic sensor may be recorded in adigital format as a time series or in the time domain, also referred toas a trace, over a given time period. In some cases, the sound orvibration energy recorded may be projected back to multiple possiblepoints of origin for the event in the underground formation. Amicroseismic event such as a fracture emits energy (e.g., sound orvibrations) that is detected and recorded by the acoustic sensor at thetop or wellhead of well 600-a. In some cases, multiple acoustic sensorsmay be utilized. For instance, each well 600 may comprise an acousticsensor at its wellhead. The microseismic data recorded at the one ormore acoustic sensors may be projected back to a possible point oforigin for event 602, for instance, based on applying one or more timeshifts.

In some circumstances, the analysis can focus on identifying distinctamplitude peaks corresponding to a fracture initiation, wherein a firstamplitude peak may correspond to an arrival time of a primary wavegenerated by the fracture initiation, while a second amplitude peak maycorrespond to an arrival time of a reflected wave (e.g., a wavereflected off a plug at an end of a current fracking stage of the well)generated by the fracture initiation, further described below inrelation to FIG. 20 . In one example, after identifying pops associatedwith fracture initiation via an FFT algorithm (or another DiscreteFourier Transform (DFT) algorithm), the distance of a fractureinitiation or cluster from a plug may be obtained (e.g., using equations(1) and (2) described above) to pin-point an origination of theshockwave (i.e., location of the fracture initiation).

FIGS. 7, 8, and 9 implement one or more aspects of the figures describedherein, including at least FIG. 6 . For instance, FIGS. 6-9 show examplepaths (i.e., path 617-a, path 617-b, path 617-c, and path 617-d)followed by the sound or vibrations from the event 602 to the heel 606-aof the first observation well 600-a. After arriving at the heel 606-a ofthe first observation well 600-a, the sound or vibration signals mayfollow the vertical wellbore (i.e., vertical region 603-a of the firstobservation well) and arrive at different times at the acoustic sensorof the wellhead. In particular, FIG. 6 shows sound or vibrations passingfrom the event 602 through fractures 616-a and 616-b of wells 600-a and600-b, respectively, as well as a small portion of rock between thosefractures. In some cases, the sound or vibrations may then travelthrough the horizontal region 604 of the well 600-a towards the heel606-a. Because much of the path in FIG. 6 involves sound passing throughfluids rather than solid rock, the path in FIG. 6 may be slower than thepaths seen in FIGS. 7-9 .

FIG. 7 shows a path slightly longer in distance than that in FIG. 6 ,but one where the sound or vibrations travel primarily through rockbetween the two wells 600-a and 600-b and then through the fluid in thewell 600-a to the heel 606-a. As previously described, despite thelonger distance traveled, the signal in FIG. 7 may arrive at theacoustic sensor before the signal in FIG. 6 , since more of this path isthrough rock than the path seen in FIG. 6 . FIGS. 8 and 9 show shorterpaths than those seen in either of FIGS. 6 and 7 . Further, the pathstraveled by the sound or vibration from event 602 in FIGS. 8 and 9 maybe primarily through rock. The combination of an overall shorter pathlength, as well as a greater path length through rock as compared tofluid, may enable the signals in FIGS. 8 and 9 to reach the acousticsensor before the signals in either of FIGS. 6 and 7 .

It should be noted that, the specifics of the FIGS. 6 through 7 are notas important as the fact that the acoustic or vibration sensor at thewellhead of the first well 600-a is likely to hear ringing, or multipleinstances of the signal (or vibrations) associated with event 602, whereeach instance arrives at a different time. In some cases, deconvolutionof the arriving signal may be used to separate each of the varioussignals following different paths and arriving from the event 602. Insome embodiments, this ringing may be correlated with ringing frompreviously-monitored events. In this way, analyzing the ringing mayallow a structure of the wells 600 and their fractures 616 to beassessed, assuming some knowledge about the rock formation. Forinstance, analysis of the ringing may provide operators with insight ona level of fracture dispersion and whether the cracks are dispersed orconcentrated. Alternatively, if some understanding of the wells 600 andfractures 616 is already known, an analysis of the ringing may helpdetermine a structure of the rock formation. In some circumstances, therock formation may not be of a uniform density, and thus, some cracksmay be softer than others. In such cases, water or other fluids flowingthrough the well bore may veer towards some cracks (e.g., softer cracks)over other cracks surrounded by a harder rock formation. Such asituation where water or fluids flow in an unequal manner towardsdifferent cracks based on their softness level may also be referred toas washout. In some cases, washout may be predicted by analyzing theringing detected at the acoustic or vibration sensor at the wellhead.

FIGS. 10-13 show a similar concept as FIGS. 6-9 , but for an offset wellevent 1002 occurring within or near an end of a fracture 1016. As shown,FIGS. 10-13 illustrate an isometric view of four offset wells 1000(e.g., offset wells 1000-a, 1000-b, 1000-c, and 1000-d), each includinga vertical (e.g., vertical regions 1003-a, 1003-b, 1003-c, and 1003-d)and a horizontal region 1004 separated by a heel 1006 (e.g., heel1006-a, 1006-b, 1006-c, 1006-d). The horizontal region 1004 of eachoffset well 1000 connects a respective heel 1006 to a toe 1001 (e.g.,toe 1001-a, toe 1001-b, toe 1001-c, toe 1001-d). In this example, anevent 1002 has occurred within or near an end of the fracture 1016,where the fracture 1016 is located near proximal to the toe 1001-b (asopposed to the heel 1006-b) of the second offset well 1000-b. In somecases, an acoustic or vibration sensor (not shown) may be coupled to atop or wellhead of the first offset well 1000-a (e.g., at a wellhead,circulating fluid line, or standpipe). The acoustic sensor may be indirect or indirect contact with a fluid in the well or wellhead, and maybe configured to measure acoustic signals in the fluid. Additionally oralternatively, a vibration sensor (not shown) may be attached to acomponent (e.g., metal component, such as a pipe) of the wellhead. Insuch cases, vibrations felt through the metal component may also bemeasured and recorded. Similar to FIGS. 6-9 , the acoustic or vibrationsensor at the well head of the observation well (i.e., first offset well1000-a) may hear ringing, or multiple instances of the signal orvibrations associated with event 1002, since each instance may arrive ata different time based on the amount of path length through rock, fluid,etc. Analysis of this ringing and time or frequency signatures obtainedfrom the signal data may allow a cluster efficiency or a distance from afrac initiation to the plug to be determined, for instance.

FIGS. 14-16 show different views of an exemplary spectral plot 1400 withfour frequency spikes associated with a frac initiation in anobservation well. This plot was created from actual acoustic sensor datataken over a period of time and then converted to a spectral plot viaFourier Transform. In this example, four frequencies dominate above thenoise baseline and three of these spikes have relatively the sameamplitude. In some cases, the amplitudes may be correlated to a size ofthe fracture (e.g., diameter, length, or volume). Further, these fourfrequencies may correspond to a formation of a fracture during one stageof a multi-stage fracturing process. While not shown, other frequencies,or other combinations of frequencies may indicate other events, such ascommunication between wells, frac initiation at an offset well, pumpdeployment at an offset well, etc. Furthermore, although spectral plot1400 shows spikes for an event in an observation well (i.e., the wellbeing monitored), in other illustrations, such spikes may representactivities in an offset well. In such cases, the events or activitiesmay be heard or felt through intervening rock/soil between theobservation and offset wells.

The spectral artifacts seen in FIGS. 14-16 are exemplary only but may beused to illustrate the analysis of any spectrum detected by the acousticsensors. For instance, each of one or more spikes in a spectrum can beclassified by intensity, amplitude, and/or stage relative perforationintensity (SRPI). There may be a separate classification for each spike,or an average of two or more spikes may be used in a classification.Each of one or more spikes may also be classified by the frac stage inwhich the spike occurred and/or a time that the spike occurred within agiven frac stage. Each of one or more spikes may also be classified by amax or central frequency as well as a frequency width (i.e., thebandwidth or distance between high and low frequencies for a spike). Insome embodiments, the spectral plot showing the frequency spikes may beused to generate a frequency signature, where the frequency signaturemay be associated with the event or activity. In some cases, thefrequency signature may be labeled by a trained model and used tofurther train the model to identify similar frequency signaturesassociated with future events. Changes in the spectrum over time mayalso be associated with a frequency signature (e.g., where a frequencypeak shifts at a recognizable rate).

In some embodiments, analysis of acoustic data may involve considerationof at least one model pertaining to the interaction of fluids withsubterranean rock. For instance, uncoupled models can be used in caseswhere the stress/displacement analysis of the reservoir rock assumesthat the rock is elastic. The fracture aperture can be computed from theelastic constants of the rock, in-situ stresses, and pressuredistribution inside the fracture. Calculation of the fluid loss to theformation can be based on Carter's 1D diffusion solution, which predictsan instantaneous leakage inversely proportional to the square root ofthe wetting time. There is no direct interaction between the diffusionand deformation processes, except for a leak-off term in themass-conservation equations of the fluid-flow analysis inside thefracture. In another example, partially coupled models can be used wherethe stress/displacement analysis is still based on the assumptions ofelasticity. The fluid loss is calculated exactly, within the frameworkof the linear diffusion law, by distributing fluid sources along thefracture. The effect of pore-pressure gradient (caused by leakoff) onrock deformation and therefore on fracture width may be accounted forwith the concept of back stress. In yet another example, fully coupledmodels can be implemented that include the full range of coupleddiffusion/deformation effects predicted by Biot's theory ofporoelasticity: sensitivity of the volumetric response of the rock tothe rate of loading, pore-pressure change induced by the variation ofmean stress, and back-stress effects already accounted for in thepartially coupled models. The fully coupled model may assist inassessing fracture quality, including a level of fracture dispersion,cluster efficiency, counts (i.e., of fractures or cracks) for eachcluster, presence of fractures that are either too long or too short, toname a few non-limiting examples.

In general, AI models aim to learn a function (f(X)) which provides themost precise correlation between input values (X) and output values (Y),such that Y=f(X). The artificial intelligence (AI) models describedthroughout this disclosure may be of a variety of types, for examplelinear regression models, logistic regression models, lineardiscriminant analysis models, decision tree models, naïve bayes models,K-nearest neighbors models, learning vector quantization models, supportvector machines, bagging and random forest models, and deep neuralnetworks.

In some embodiments, a plurality of distinct machine-learning algorithmsmay operate in parallel, which may serve to enhance the accuracy of thetechniques described herein. In some aspects, the use of multiplemachine-learning algorithms may also decrease false positive indicationsas compared to the use of a single machine learning algorithm. In somecases, a combination of three or four machine learning algorithms mayoperate in parallel, which may provide a balance of high accuracy versussystem complexity. Some non-limiting examples of machine learningalgorithms may include a neural network, a decision tree, a supportvector machine, and Bayesian methods.

Cluster Efficiency

This disclosure now turns to using the acquisition and analysis of highfrequency acoustic or vibration data in the time and/or frequency domainto provide real-time quantitative feedback on fracking operations (suchas, but not limited to, fracture initiation or formation, number/countper cluster, and fracture dispersion). Whereas traditional subterraneananalysis, such as microseismic monitoring, takes in massive amounts ofdata, requiring slow off-site computation, high frequency acoustic orvibration data can provide greater insights with less data processing byusing one sensor per well. Further, since the processing requirementsare significantly lower as compared to traditional techniques, feedbackmay be in real-time (or close to real time), and processing may beperformed on cheaper, less computationally powerful, on-site computers.In some embodiments, a single acoustic or vibration sensor may be usedto provide both position and quality information about cracks. In somecases, one or more of the following parameters may be determined for agiven crack or a set of cracks: (1) connection to the well; (2)connection to another crack; (3) diameter; (4) length; (5) whether thecrack has been propped or initiated; (6) quality and/or volume of fluidflow within the crack; (7) number; (8) location and/or depth, forinstance with respect to the plug or the surface; and (9) cross-section.

In an embodiment, individual cracks can be identified in the frequencydomain of the acoustic/vibration data. In some cases, theacoustic/vibration signal data may comprise a plurality of frequencydomain features, where the frequency domain features are indicative ofthe acoustic or vibration signal across a frequency spectrum. Bycomparing the plurality of frequency domain features with one or moreknown frac initiation signatures and/or frac dispersion signatures, alevel of frac dispersion (i.e., are cracks disperses or concentrated)may also be obtained. As previously noted, a Fourier Transform may beused to generate a frequency domain representation of the time domaindata, for instance. In some embodiments, a Short Time Fourier Transform(STFT) technique, a Discrete Fourier Transform (DFT) technique, or aFast Fourier Transform (FFT) algorithm may be used for the Fourieranalysis.

In some other cases, “pops” or acoustic vibrations generated by afracture initiation may be measured and recorded by theacoustic/vibration sensor. As noted above, one or more distinct waves(i.e., primary waves, reflected waves) may be measured and identified byanalyzing an electrical signal in the time domain for a window of time.For instance, two amplitude peaks corresponding to the fractureinitiation may be identified, where a first of the two amplitude peakscorresponds to an arrival time of a primary wave generated by thefracture initiation, while a second of the two amplitude peakscorresponds to an arrival time of a reflected wave generated by thefracture initiation and reflected off a plug at an end of a currentfracking stage of the well. By measuring a time difference between whenthe two amplitude peaks arrive at the sensor and performing one or moreoperations shown in equations (1) and (2), above, a location of thefracture initiation may be estimated. The location of the fractureinitiation may be based at least in part on the distance between thefracture initiation and the plug. Further, the distance between thefracture initiation and the plug may be calculated by dividing the timedifference between the arrival of the first and second amplitude peaksby two and multiplying that result by a speed of sound in the frackingfluid.

FIG. 17 shows an embodiment of a method 1700 for determining clusterefficiency and dispersion of fractures during hydraulic fracturingoperations. Optionally method 1700 may also be employed to adjust one ormore parameters of the hydraulic fracturing operations based on acluster map of fracture initiations in a stage of a well. The method1700 will be described in association with components of the system 1800shown in FIG. 18 and the system 1900 shown in FIG. 19 . The method 1700can include collecting high frequency acoustic or vibration data at anacoustic sensor (Block 1702), for instance, via one or more acoustic orvibration sensors 1808 and/or 1904. Optionally, the method 1700 may alsoinvolve monitoring and recording static pressure readings and/or changesin static pressure (Block 1702). In some embodiments, conversion to afrequency spectrum may be performed via a spectrum analyzer 1910 orother device for converting data from the time domain to frequencydomain (e.g., the optional acoustic/vibration data converting module1814). In some cases, conversion may involve performing a FFT on thetime domain data to generate frequency domain data. For example, FFT oranother transform may be performed to identify the acoustic vibrationsassociated with fracture initiation.

In some examples, the data from the acoustic or vibration sensor may befed into a model for identifying spectral aspects of the data that maymatch (or resemble) known acoustic or vibration behavior of an event(Block 1704). For instance, the spectrum analyzer 1910 may passfrequency spectrum data to the machine learning system 1912 and/or themodel 1914 to detect a frequency signature deserving further analysis.Additionally or alternatively, the data comparing module 1816 may alsobe used to perform this detection. For instance, background noise may beassociated with a rounded peak around 1 kHz. In one example, amplitudespikes observed at around 5 and/or 10 kHz may be identified as eventsfor comparing to known signatures in the model. In other words, themodel may been previously trained to recognize a spectral signalassociated with certain fracking events or crack types or crackparameters. Besides analyzing and assessing frequency spectrum data, themodel may optionally be used to analyze changes in static pressure takenby the acoustic or vibration sensor (e.g., see FIG. 17 ), or by a secondsensor that tracks pressure rather than acoustic or vibration data.

In some examples, the model may then classify the fracking event (Block1706), for instance, by matching the sensor data with one or morecategories of events. For instance, the model and/or a label assignmentmodule 1818 may match the acquired sensor data with previous frequencydomain spectra measured as a result of previous fracture initiations andpreviously classified by the machine-learning system. Additionally oralternatively, the model and/or the label assignment module 1818 maymatch the acquired sensor data with a category of crack sizes or cracklengths previously classified by the machine-learning system. In oneexample, a 5 kHz spike in the frequency domain may be associated with aformation of cracks, while a 10 kHz spike may generally be associatedwith an intersection of a crack and the main well. In this case, thelabel assignment module 1818 may match the acquired sensor data withthese known frequency spikes. Additionally, in some embodiments, theremay be smaller additional frequency spikes that the model has associatedwith fluid flow dynamics such as laminar versus turbulent flows. Thus,the event data can be compared to known frequency spectrum signaturesfor small crack formation, large crack formation, short crack formation,long crack formation, turbulent fluid flow in a crack, laminar fluidflow in a crack, crack formation that intersects the well, crackformation that does not intersect the well, and horizontal shifting, toname a few non-limiting examples. In some embodiments, existingcategories or classifications may be stored in the model 1914.

As part of the feedback track of the method 1700, at least two amplitudepeaks corresponding to the fracture initiation may be identified (Block1712), for instance, if the model classified the event as a fractureinitiation at Block 1706. Further, the method 1700 may includedetermining a distance from the fracture initiation to a plug within awellbore or borehole (Block 1714), as previously described above. Insome cases, the distance from the fracture initiation to the plug withinthe wellbore may be based at least in part on a time between arrival ofthe two amplitude peaks at the acoustic/vibration sensor, where thefirst amplitude peak corresponds to an arrival time of a primary wavegenerated by the fracture initiation, and the second amplitude peakcorresponds to an arrival time of a reflected wave generated by thefracture initiation and reflected off the plug. In some embodiments, theplug may be located at an end of a current fracking stage of the well.

In some embodiments, a cluster map of fracture initiations in the stageof the well may be created based at least in part on the distance fromthe fracture initiation to the plug (Block 1718). Furthermore, one ormore parameters of the hydraulic fracturing operations may be adjustedbased at least in part on the cluster map, which may facilitate a moreeven dispersion of fractures within fracture clusters of a subsequentstage.

In some cases, cluster map module 1836 may be used to present a clustermap of fracture initiations in the stage of the well to operators, forinstance, via an operator display 1916. In some cases, an operator mayinput manual changes to operations (e.g., adjust a parameter of thehydraulic fracturing operations) in response to different cluster maps,where the manual changes may be relayed to the controller 1918.Optionally, in parallel to presenting the cluster map via the display1916, or alternatively, automated control of the controller 1918 can beperformed (Block 1720). In some cases, the feedback/control module 1822may be configured to automatically control fracking operations, forinstance, via the controller 1918. In some instances, an algorithm maybe used in conjunction with the feedback/control module 1822 and/or thecontroller 1918 to achieve a more even dispersion of fractures withinfracture clusters (e.g., of a subsequent stage). For instance, thealgorithm may analyze the resulting cluster map and adjust future fracstage parameters to optimize fracture dispersion within fractureclusters. In some embodiments, a count (i.e., of fractures or cracks)may be determined for each cluster. Further, a higher count in one areaas compared to other areas in a stage may be indicative of the fracturesor cracks in the area being too long (i.e., above a threshold). In suchcases, the algorithm may be used to determine optimal frac stageparameter values, for instance, to ensure a more even distribution offractures or cracks for each cluster. As shown, the method 1700 may thenreturn to Block 1702 for collection of more data.

In some embodiments, after the model classifies events (Block 1706), themethod 1700 may include collecting results data (i.e., as part of themodel training track of the method 1700), where the results data may beassociated with the event (Block 1708). It should be noted that, thetraining and feedback tracks may operate serially, or in parallel, basedon use case. In some other cases, the training and feedback tracks maybe alternatives. In some cases, results data may include any data typeor information produced as a consequence of the event classified inBlock 1706. For instance, results data could relate to increased oilflow in the well, during production, following fracture initiation inone or more perforation clusters of a stage of well, formation of one ormore cracks during fracking, etc. In some other cases, results data mayrelate to a decrease in fracking fluid pressure following formation of acrack, to name another non-limiting example. In some embodiments,results data may be obtained from external resources 1834 such as a flowmeter measuring oil/gas volume during production. In some cases, themodel (e.g., model 1914) may then analyze the data to determinecorrelations (if any) between the results data and the classified eventdata (Block 1710). In some cases, the data comparing module 1816 (oranother applicable module) may be configured to determine saidcorrelations. In some cases, multiple sets of results data may becorrelated to a single classified event. For instance, the method 1700may be used to determine that frequency signatures classified as largecrack formation may correlate to increased oil flow, whereas frequencysignatures classified as small crack formation may correlate to steadyoil flows. In some cases, these correlations may be used to train themodel, following which the method 1700 may restart. In some cases, themodel training module 1830 may be used to train the model 1914 (Block1716).

In FIG. 18 , the order of the blocks within the machine-readableinstructions 1806 is non-limiting. For instance, the model trainingmodule 1830 can operate to train the model either before, after, or inparallel to operation of fracture initiation module 1819, plug distancemodule 1820, and/or feedback/control module 1822.

The following provides a more detailed description of the system 1800shown in FIG. 18 , where FIG. 18 illustrates a more detailed embodimentof some example components that may be used to carry out the methodshown in FIG. 17 and/or to underly the components shown in FIGS. 19 and20 . Specifically, FIG. 18 illustrates a system 1800 configured fordetermining cluster efficiency, and optionally controlling frackingoperations to facilitate an even dispersion of fractures (e.g., fractureor crack count for each cluster in a multi-cluster stage isapproximately the same), in accordance with one or more implementations.In some implementations, system 1800 may include one or more computingplatforms 1802. Computing platform(s) 1802 may be configured tocommunicate with one or more remote platforms 1804 according to aclient/server architecture, a peer-to-peer architecture, and/or otherarchitectures. Remote platform(s) 1804 may be configured to communicatewith other remote platforms via computing platform(s) 1802 and/oraccording to a client/server architecture, a peer-to-peer architecture,and/or other architectures. Users or operators may access system 1800via remote platform(s) 1804.

Computing platform(s) 1802 may be configured by machine-readableinstructions 1806. Machine-readable instructions 1806 may include one ormore instruction modules. The instruction modules may include computerprogram modules. The instruction modules may include one or more ofacoustic or vibration data acquiring module 1810, acoustic or vibrationdata transferring module 1812, acoustic or vibration data convertingmodule 1814 (optional), data comparing module 1816, label assignmentmodule 1818, fracture initiation module 1819, plug distance module 1820,feedback/control module 1822, model training module 1830, cluster mapmodule 1836, and/or speed determining module 1826 to name a fewnon-limiting examples.

Acoustic or vibration sensor(s) 1808, previously described above, may bein communication with the computing platform(s) 1802 and may beconfigured to provide raw data to the processor(s) 1838. In someembodiments, the acoustic or vibration sensor(s) 1808 may be adapted fordirect physical contact with fluid within a well (or alternatively, fordirect physical contact with a component of the well such as a pipe). Insome examples, the acoustic or vibration sensor 1808 may measureacoustic vibrations in fracking fluid in a wellhead, circulating fluidline, or standpipe of the well. In some examples, the sensor(s) 1808 maybe high frequency sensors, for instance, designed for >1000sample/second rate. In an embodiment, the acoustic or vibration sensor1808 may include a piezoelectric material configured to generate acurrent or voltage proportional to an amplitude of vibration of thepiezoelectric material. Some non-limiting examples of piezoelectricmaterials may include lead zirconate titanate (PZT), barium titanate,lead titanate, Rochelle salt, ammonium dihydrogen phosphate, lithiumsulphate, quartz, topaz, zinc oxide, etc.

In some examples, acoustic or vibration data acquiring module 1810 maybe configured to acquire acoustic or vibration data in a time domainfrom the sensor(s) 1808. For instance, the acoustic or vibration dataacquiring module 1810 may be configured to convert the acousticvibrations into an electrical signal in a time domain.

In some examples, acoustic vibration data transferring module 1812 maybe configured to transfer the acoustic or vibration data to a spectrumanalyzer (e.g., spectrum analyzer 1910) or any another device capable oftransforming data from the time domain to the frequency domain. Itshould be noted that, the spectrum analyzer may or may not be part ofthe same computing platform that various other modules in FIG. 18 are apart of. For instance, the spectrum analyzer may be separate from acomputing platform where comparisons of frequency signatures to themodel occur. In some embodiments, acoustic or vibration data convertingmodule 1814 may be configured to convert the acoustic or vibration datafrom the time domain to a frequency domain via the spectrum analyzer oranother applicable device.

Fracture initiation module 1819 may be configured to identify a fractureinitiation from the current frequency domain spectrum via amachine-learning system trained on previous frequency domain spectrameasured as a result of previous fracture initiations and previouslyclassified by the machine-learning system. In some cases, fractureinitiation module 1819 may be configured to perform similar functions asone or more of the data comparing module 1816 and the label assignmentmodule 1818. Additionally or alternatively, fracture initiation module1819 may also be configured to analyze the electrical signal in the timedomain during the window of time to identify two amplitude peakscorresponding to the fracture initiation found in the current frequencydomain spectrum. In some embodiments, fracture initiation module mayalso measure a time between the two amplitude peaks and divide the timeby two to give a result. This result may be passed on to the plugdistance module 1820 for further computation.

Data comparing module 1816 may be configured to compare the acoustic orvibration data in the frequency domain to a model trained on frequencysignatures, where the frequency signatures correspond to fractureinitiation and/or to known crack types or qualities (e.g., connected towell, able to be propped, etc.). In some examples, the model may betrained to recognize frequency signatures corresponding to certainfracking fluid flow patterns. By way of non-limiting example, thecomparing may comprise consideration of a number of frequency spikes, awidth of the frequency spikes, and/or an amplitude of the frequencyspikes pertaining to the frequency signatures, among other aspects ofthe frequency spectrum. In some embodiments, a fracture initiation canbe identified from a plurality of frequency peaks in the spectrum as thepeak having the greatest amplitude.

Label assignment module 1818 may be configured to assign one of aplurality of labels to the acoustic or vibration data in the frequencydomain based on the comparing. By way of non-limiting example, theplurality of labels may include: fracture initiation, connected to thewell, connected to another crack, diameter of the crack, length of thecrack, and whether the crack has been propped. In some cases, theplurality of labels may be associated with binary values (i.e., 1 or 0,True or False, Yes or No), for instance, connected to the well or toanother crack. Alternatively, the labels may be associated withnumerical or alpha-numerical values, where a corresponding unit (e.g.,mm, cm, ft) may be implied or explicitly stated. For instance, for thelabel “diameter of the crack” or “length of the crack”, the labelassignment module 1818 may assign a label of “4” if a unit (e.g., cm) isinherently implied. Alternatively, the label assignment module 1818 mayassign a label of “4 cm” if the unit needs to be explicitly stated.

Plug distance module 1820 may be configured to compute a distance fromthe fracture initiation to the plug and display the computed distance onthe operator display (e.g., operator display 1916). For instance, theplug distance module 1820 may multiply the result from the fractureinitiation module 1819 with the speed of sound in the fracking fluidfrom the speed determining module 1826 to determine the distance fromthe frac initiation to the plug.

In some cases, feedback/control module 1822 may be configured toinstruct a controller (e.g., controller 1918) to make adjustments tocurrent or future fracking operations, for instance, in response toanalysis of the cluster map generated by the cluster map module 1836. Insome cases, the adjustments may include varying a parameter of thehydraulic fracturing operations to achieve a more even dispersion offractures within fracture clusters of a subsequent stage, for instance.Additionally or alternatively, the feedback/control module 1822 may alsobe configured to control the pump and instruct it to start pumpingfracking fluid into a stage of the well.

In some embodiments, model training module 1830 may be configured totrain the model (also shown as model 1914 in FIG. 19 ) for recognizingacoustic or vibration data in the frequency domain using one or more ofthe plurality of labels or classifications.

In some embodiments, speed determining module 1826 may be configured tocompute the speed of sound in the fracking fluid thought to be near thefracture initiation, which may then be used by the plug distance module1820 to give a distance from the fracture initiation to the plug.

In some implementations, computing platform(s) 1802, remote platform(s)1804, and/or external resources 1834 may be operatively linked via oneor more electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which computing platform(s) 1802,remote platform(s) 1804, and/or external resources 1834 may beoperatively linked via some other communication media.

A given remote platform 1804 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given remote platform 1804 to interface with system 1800 and/orexternal resources 1834, and/or provide other functionality attributedherein to remote platform(s) 1804. By way of non-limiting example, agiven remote platform 1804 and/or a given computing platform 1802 mayinclude one or more of a server, a desktop computer, a laptop computer,a handheld computer, a tablet computing platform, a NetBook, aSmartphone, a gaming console, and/or other computing platforms.

External resources 1834 may include sources of information outside ofsystem 1800, external entities participating with system 1800, and/orother resources. For instance, external data may be fed into the modelto help with initial training. In some implementations, some or all ofthe functionality attributed herein to external resources 1834 may beprovided by resources included in system 1800. One non-limiting exampleof an external resource is results data, such as oil/gas flow volumethat may be measured by one or more sensors other than the acoustic orvibration sensor.

Computing platform(s) 1802 may include electronic storage 1840, one ormore processors 1838, and/or other components. Computing platform(s)1802 may include communication lines, or ports to enable the exchange ofinformation with a network and/or other computing platforms.Illustration of computing platform(s) 1802 in FIG. 18 is not intended tobe limiting. Computing platform(s) 1802 may include a plurality ofhardware, software, and/or firmware components operating together toprovide the functionality attributed herein to computing platform(s)1802. For example, computing platform(s) 1802 may be implemented by acloud of computing platforms operating together as computing platform(s)1802. In an embodiment, the computing platform 1802 including theprocessor(s) 1838 may reside on the premises of the fracking operation,for instance, on the same pad as the well(s) being monitored/controlled.

Electronic storage 1840 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 1840 may include one or both of system storage thatis provided integrally (i.e., substantially non-removable) withcomputing platform(s) 1802 and/or removable storage that is removablyconnectable to computing platform(s) 1802 via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). Electronic storage 1840 may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage1840 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 1840 may store software algorithms,information determined by processor(s) 1838, information received fromcomputing platform(s) 1802, information received from remote platform(s)1804, and/or other information that enables computing platform(s) 1802to function as described herein.

Processor(s) 1838 may be configured to provide information processingcapabilities in computing platform(s) 1802. As such, processor(s) 1838may include one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Althoughprocessor(s) 1838 is shown in FIG. 18 as a single entity, this is forillustrative purposes only. In some implementations, processor(s) 1838may include a plurality of processing units. These processing units maybe physically located within the same device, or processor(s) 1838 mayrepresent processing functionality of a plurality of devices operatingin coordination. Processor(s) 1838 may be configured to execute modules1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822, 1830, 1826, 1836, and/orother modules. Processor(s) 1838 may be configured to execute modules1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822, 1830, 1826, 1836, and/orother modules by software; hardware; firmware; some combination ofsoftware, hardware, and/or firmware; and/or other mechanisms forconfiguring processing capabilities on processor(s) 1838. As usedherein, the term “module” may refer to any component or set ofcomponents that perform the functionality attributed to the module. Thismay include one or more physical processors during execution ofprocessor readable instructions, the processor readable instructions,circuitry, hardware, storage media, or any other components.

It should be appreciated that although modules 1810, 1812, 1814, 1816,1818, 1819, 1820, 1822, 1830, 1826, and/or 1836 are illustrated in FIG.18 as being implemented within a single processing unit, inimplementations in which processor(s) 1838 includes multiple processingunits, one or more of modules 1810, 1812, 1814, 1816, 1818, 1819, 1820,1822, 1826, 1836, and/or 1830 may be implemented remotely from the othermodules. The description of the functionality provided by the differentmodules 1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822, 1826, 1836,and/or 1830 described herein is for illustrative purposes, and is notintended to be limiting, as any of modules 1810, 1812, 1814, 1816, 1818,1819, 1820, 1822, 1826, 1836, and/or 1830 may provide more or lessfunctionality than is described. For example, one or more of modules1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822, 1826, 1836, and/or 1830may be eliminated, and some or all of its functionality may be providedby other ones of modules 1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822,1826, 1836, and/or 1830. As another example, processor(s) 1838 may beconfigured to execute one or more additional modules that may performsome or all of the functionality attributed below to one of modules1810, 1812, 1814, 1816, 1818, 1819, 1820, 1822, 1826, 1836, and/or 1830.

FIG. 19 illustrates a drilling system 1900 comprising an exemplary well1902. In some examples, the well 1902 may comprise a wellhead 1920 andan acoustic or vibration sensor 1904 at the wellhead, where the acousticor vibration sensor 1904 is in direct physical contact with fluid in thewell. The acoustic or vibration sensor 1904 may be configured to measureacoustic vibrations in fracking fluid in the wellhead 1920, oralternatively, a circulating fluid line, or standpipe of the well 1902.In some cases, the drilling system 1900 may further comprise aperforation gun 1906 and a plug 1907 deployed at a stage in the well1902. As shown, the perforation gun 1906 and the plug 1907 may be headeddownhole and controlled via a wireline 1908. In some cases, one or morefractures and cracks having varying characteristics may be created dueto ongoing fracking, for instance, via firing the perforating gun 1906and pumping fracking fluid into the stage of the well 1902. In someembodiments, firing the perforating gun 1906 may set off a charge thatcauses perforation clusters or holes to form through the wellbore casingand cement and into the subterranean formation (e.g., shale), as shownin more detail in FIG. 20 . Further, once high-pressure fracking fluidand/or proppant is pumped into the stage of the well, the pressureforces the fluid out into the perforation clusters and into thesubterranean formation causing it to fracture. For instance, and asillustrated in FIG. 19 , a number of cracks may be created, including acrack 1950-a that intersects the well 1902 but is too narrow to bepropped, another crack 1950-b that intersects the well 1902 and is largeenough to be propped, and one or more cracks 1950-c that do notintersect the well 1902. Although the perforation gun 1906 isillustrated in a vertical section of well, it should be noted that, inother embodiments, the perforation gun 1906 may be located in ahorizontal or roughly horizontal section of the well 1902 (e.g.,horizontal region 604 in FIG. 6 ).

The acoustic or vibration sensor 1913 may provide raw data (e.g., in atime domain) to a spectrum analyzer 1910 (optional) arranged on-site (oroptionally at a location remote from the well pad). In some embodiments,the spectrum analyzer 1910 may be configured to convert the raw timeseries data to a frequency domain. A machine learning system 1912,including a model 1914, may be configured to search for and identifyfrequency signatures in the frequency domain of the data. In someembodiments, the model 1914 may be stored in electronic storage 1840. Insome cases, the machine learning system 1912 may be configured to searchfor and identify time signatures in the raw data, without the use of thespectrum analyzer 1910. In some circumstances, even though conversionfrom time domain to frequency domain may not be performed for analysisor training the model, conversion may be performed prior to display onthe operator's computer, since frequency domain representation may bemore intuitive or easier to understand for a human.

In some cases, the identified frequency signatures may be associatedwith known frequency signatures for different fracking events andparameters, including, but not limited to, fracture initiation and/orcrack parameters. For instance, water and proppant passing throughsmaller cracks, such as crack 1950-a, may generate higher frequency dataand/or vibrations than larger cracks, such as crack 1950-b. As describedin relation to FIGS. 6-13 , pressure or sound waves may travel atdifferent speeds through dense rock, sand, air, fluids in a fracture orwell, etc. In such cases, the speed at which pressure or sound wavestravel through the fracking fluid may need to be calculated, forinstance, to determine a distance of the fracture initiation from theplug 1907.

In some circumstances, the model 1914 may be trained to recognizefrequency signatures, for instance, associated with a fractureinitiation from the current frequency domain spectrum via themachine-learning system 1912 and the model 1914. In some cases, themachine-learning system 1912 may be trained on previous frequency domainspectra measured as a result of previous fracture initiations andpreviously classified by the machine-learning system 1912.

After (or in parallel to) training the model 1914, the machine learningsystem 1912 may utilize one or more of the fracture initiation module1819, the plug distance module 1820, the cluster map module 1836, and/orthe speed determining module 1826 (described above in relation to FIG.18 ) to analyze the electrical signal from the acoustic/vibration sensor1904 in the time domain. The analysis may be used to obtain one or moreof the speed of sound in the fracking fluid and a distance from thefracture initiation to the plug 1907. In some embodiments, the clustermap module 1836 may use the distance to create a cluster map of fractureinitiations in the stage of the well 1902.

In some embodiments, at least the distance and the cluster map may bepassed to an operator's computer and display 1916 to enable the operatorto make manual adjustments to well operations via the controller 1918.Additionally or alternatively, the machine learning system 1912 may beconfigured to automatically adjust fracking operations through thecontroller 1918. Regardless of manual or automated control, thecontroller 1918 may be instructed to utilize different pressures ondifferent stages, or different stage durations, and adjust pressure orduration for future stages based on previous stages. In other words, oneor more pressure or duration adjustments may be made for future stagesbased on settings from previous stages that produced an even dispersionof fractures and the highest cluster efficiency. In some embodiments,the perforation gun 1906 may go to different potential cluster locationswithin a stage prior to firing. Adjusting fracking operations mayinvolve adjusting one or more downhole parameters through the controller1918 (if automatic control is enabled), or providing one or moresuggestions to the operator (if manual control). Some non-limitingexamples of downhole parameters may relate to distance of perforationgun 1906 from the plug 1907 (i.e., to allow for a more even dispersionof fractures), frac stage time, timing of proppant release, controllingperf gun firing (e.g., power), start and end of pumping down perfgun(s), start and end of pumping down plugs, pressurizing frack fluid toinitiate creation of fractures, perf gun pressure level, pH of fluidsforced into the formation, etc.

FIG. 20 illustrates a drilling system 2000 for determining clusterefficiency during hydraulic fracturing operations. In some cases,drilling system 2000 may be also deployed to disperse fractures moreevenly within fracture clusters during hydraulic fracturing operations,in accordance with one or more implementations. In some examples,drilling system 2000 may implement one or more aspects of the figuresdescribed herein. As shown, drilling system 2000 comprises a wellbore2001 (also referred to as a bore hole), drilling rig 2003, a pump truck2004, a wellhead assembly comprising a well head 2006, and an acousticor vibration sensor 2002. In the example shown, the acoustic orvibration sensor 2002 may be coupled to one of the interfaces of thewellhead 2006, such that the acoustic or vibration sensor 2002 is indirect physical contact with fluid in the wellhead 2006. In some othercases, the sensor 2002 may be coupled to a circulating fluid line 2009,or even a standpipe of the well. In either case, the sensor 2002 may beconfigured to convert acoustic vibrations measured in fracking fluid inthe fracking wellhead 2006, the circulating fluid line 2009, or thestandpipe into an electrical signal in the time domain. In someexamples, the acoustic or vibration sensor 2002 may comprise wirelesstransmission hardware for supporting communication with a spokecomputer, hub, or wide area network (not shown). In some cases, theacoustic sensor/vibration sensor 2002 samples at greater than 1 kHz.Furthermore, the acoustic/vibration sensor 2002 may be configured to bein contact with the fracking fluid in the well or circulating fluid lineor the standpipe at the wellhead, or with a surface of the wellhead,circulating fluid line, or standpipe. In some cases, pressure sensordata (i.e., in addition to analyzing acoustic/vibration data, or as analternative) may be measured and analyzed during the window of time.

Similar to FIG. 6 , the wellbore 2001 may include a vertical and ahorizontal region separated by a heel 2026. The horizontal region mayextend beyond the figure to the right and a plug 2007 may separate theillustrated stage from additional stages further down the well (off thepage to the right). In some cases, cluster spacing and dispersion offractures plays a significant role in the well's 2000 performance. Forinstance, if the cluster spacing is too small (i.e., under a minimumthreshold), the stimulated area between major fractures may overlap,which may adversely impact the efficiency of fracturing stimulation. Inother cases, if the cluster spacing is too large (i.e., above a maximumthreshold), the area between major fractures cannot be stimulatedcompletely, which may impact reservoir recovery extent. While evenfracture dispersion is the goal, often times it is not the case withcurrently used techniques. In particular, when fracking fluid is pumpeddown into the bore hole, a downhole cluster further away from the heel(e.g., the cluster near the plug 2007) may receive a lower volume orpressure of fracking fluid than an uphole cluster closer to the heel2026. In such cases, fractures may be induced more easily in the upholecluster. In some circumstances, a majority of the fracking fluid mayonly go into 1 or 2 clusters of a multi-cluster stage (e.g., 3 or 4cluster stage).

FIG. 20 depicts one stage of a multi-cluster stage following clusterperforating. Cluster perforating may refer to the formation of multiplesets of perforations (e.g., perforations 2016). These perforations inthe casing may also extend a short distance into the formation as shown,and they are often formed in clusters 2010, each cluster 2010corresponding to a position of a perforation gun when fired. Afterperforations are made, the plug 2007 is sent downhole until it reachesan end of a stage to be fracked (as shown). Fracking fluid is thenpumped into the borehole 2001 and passes through the perforations 2016and begins expanding the perforations into fractures 2005 (only onefracture 2005 is shown, but in practice each perforation 2016 wouldlikely see some fracture initiation, and thus clusters of fractureswould form). For instance, after setting the plug 2007 to isolate thestage, multiple sets of perforations 2016 were fired via the perforationgun (e.g., shown as perf gun 1906 in FIG. 19 ). In the example shown,three sets of perforations 2016 comprising 4 perforations each have beenfired to form three clusters 2010. In some embodiments, pump truck 2004may be used to pump high-pressure fracking fluid down the borehole toinduce fractures in the shale formation. A cluster can refer to acluster of perforations or a cluster of fractures forming from theperforations. Cluster efficiency, however, refers to the distribution offractures.

As shown, a fracture initiation has occurred (e.g., see fracture 2005)in a first cluster 2010 near the heel 2026. In some cases, fractures,such as fracture 2005, may be formed every few seconds (e.g., 2 seconds)once fracking fluid is pumped down into the borehole 2001. As notedabove, in some embodiments, acoustic vibrations may be measured infracking fluid in the wellhead 2006, the circulating fluid line 2009, ora standpipe of the well. In some cases, the electric signal from thesensor 2002 may be recorded to a memory, and a fracture initiation(e.g., of fracture 2005) may be identified from the time domainspectrum, or alternatively, the frequency domain spectrum. In somecases, a machine-learning system deploying an artificial intelligencealgorithm, a neural network, etc., trained on previous frequency domainspectra measured as a result of previous fracture initiations andpreviously classified by the machine-learning system may be utilized forthe identification. In some cases, the machine-learning system may behosted on a local server, or alternatively, on a remote server in adifferent location from the drilling system 2000. If the latter, theelectrical signal may be transported via a large area network to aremote-machine learning system to perform the frac initiationidentification.

In some cases, one or more amplitude peaks corresponding to the fractureinitiation may also be identified based on analyzing the electricalsignal in the time domain during a window of time. In other words,following frequency domain analysis (i.e., to identify the fractureinitiation), the raw time domain data measured by the acoustic/vibrationsensor 2002 may be further analyzed to identify distinct amplitude peaksassociated with a primary wave (Q₁) directly traveling uphole from theinitiated fracture 2005 and a secondary wave (Q₂) first travelingdownhole from the initiated fracture 2005, and then reflecting off ofthe plug 2007 (reflected wave Q_(2′)) and then traveling upholefollowing the primary wave (Q₁). The primary wave Q₁ and the reflectedwave (Q_(2′)) may arrive at different times at the acoustic/vibrationsensor 2002 since the distance traveled by them is also different.Further, the primary wave (Q₁) may be of a higher amplitude than thereflected wave since it follows a more direct path to theacoustic/vibration sensor. In some embodiments, the two amplitude peaksmay be identified based on them exceeding an amplitude threshold.Additionally or alternatively, the two amplitude peaks may be identifiedbased on a combination of an amplitude and/or bandwidth. In yet othercases, the amplitude peaks may be identified based on a frequency of thetwo amplitude peaks. In some embodiments, the analyzing the electricalsignal for the window of time may be performed in the frequency domainto identify the two amplitude peaks. For instance, the electrical signalmay be transformed from the time domain to the frequency domain for thewindow of time, and the two amplitude peaks may be identified bycomparing the electrical signal in the frequency domain for the windowof time to frequency domain data associated with previous pairs ofamplitude peaks corresponding to previous fracture initiations.

As described above, by measuring a time span between the two amplitudepeaks, dividing it by two, and further multiplying it by the speed ofsound in the fracking fluid, a distance (shown as D1 in FIG. 20 ) fromthe fracture initiation to the plug may be calculated. In some cases,measuring a time between the two amplitude peaks comprises measuring atime between a maximum of a first of the two peaks and a maximum of asecond of the two peaks. In some cases, the speed of sound in thefracking fluid may be a function of the arrival time of the primary wavefrom the fracture initiation and the arrival time of the reflected wavefrom the fracture initiation after reflecting off the plug, shown inequation (2) above. Further, since the location of the plug is known, alocation of the fracture initiation (e.g., of fracture 2005) may bedetermined based on the distance between the fracture initiation and theplug. In some cases, the distance from the fracture initiation to theplug and/or the location of the fracture initiation may be displayed toan operator on a visual display of a user/operator device (not shown).The user/operator device may be selected from a group consisting of asmart phone, a laptop, a computer, a tablet, a NetBook, or any otherapplicable device. In some cases, the distance from the fractureinitiation to the plug may be stored with other distances from priorfracture initiations to corresponding plugs, and referenced to differentplug positions such that a mapping of fracture initiations in clustersassociated with each plug position may be determined for a frackingstage. For instance, the distance (D1) from the fracture initiation tothe plug 2007 may be stored with reference to the position of plug 2007in a database, where the database may comprise other fracture initiationdistances (D) and corresponding plug positions.

In some other cases, the perf gun may be controlled (e.g., via thewireline) to go to different potential cluster locations within a stage,such as a subsequent stage, based on the cluster map of fractureinitiation distances and plug positions from previous stages. In thisway, more even or more ideal fracture dispersion may be achieved bydetermining an optimal location for firing the perf gun (i.e., to formclusters) relative to where the plug is positioned. Ideal fracturedispersion may depend on the soil/formation structure andcomposition—and thus approaching an ideal fracture dispersion mayinvolve increasing fracture spacing or decreasing fracture spacing basedon analysis of cluster efficiency in a previous stage of a well or apreviously fracked nearby well. It should be noted that while only asingle fracture initiation is illustrated in FIG. 20 , fractureinitiation may be performed in each cluster 2010 of the stage. Forinstance, in some embodiments, fracture initiation may occur in eachcluster of the stage once fracking fluid is pumped into the borehole. Insome other cases, after fracture initiation in the first cluster, theplug 2007 may be moved uphole to initiate fractures in the secondcluster, and so on. In such cases, the distance from the fractureinitiation to the plug for the second and third clusters may also bestored in the database and referenced to the respective plug position.Thus, a mapping of fracture initiations in the different clustersassociated with each plug position may be determined for the frackingstage.

FIG. 21 illustrates a method of measuring and/or improving clusterefficiency. The method 2100 can begin by measuring acoustic vibrationsin fracking fluid in a fracking wellhead, circulating fluid line, orstandpipe of a well (Block 2102) and then converting the acousticvibrations into an electrical signal in a time domain (Block 2104).These electrical signals can be recorded to memory (Block 2106) andanalyzed in the time domain for a window of time (Block 2108). Thisanalysis can seek to identify two amplitude peaks corresponding to afracture initiation (Block 2108), and in some cases can also make use ofa frequency spectrum to help identify the fracture initiation. Themethod 2100 can then measure a time between the two amplitude peaks(Block 2110) and divide the time by two to give a result (Block 2112).This result can be multiplied by a speed of sound in the fracking fluidto give a distance between the fracture initiation and a plug at an endof a current fracking stage of the well (Block 2114). Finally, thelocation of the fracture initiation can be returned to an operator basedon the distance between the fracture initiation and the plug (Block2116) and this method 2100 can be repeated for a plurality of fractureinitiations to form a map of the fractures or a value representing thecluster efficiency of a stage or well (Block 2118).

Additional Embodiments

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein thesensor samples at greater than 1 kHz. In some examples of the method,system, and non-transient computer-readable storage medium describedherein the sensor is an acoustic sensor.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein the twoamplitude peaks are identified based on exceeding an amplitudethreshold. In some examples of the method, system, computing platform,and non-transient computer-readable storage medium described herein thetwo amplitude peaks are identified based on a combination of amplitudeand width. In some examples of the method, system, computing platform,and non-transient computer-readable storage medium described herein thetwo amplitude peaks are identified based on a frequency of the twoamplitude peaks.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein thesensor is configured to be in contact with the fracking fluid in thewell or with a surface of the circulating fluid line or the standpipe atthe wellhead.

Some examples of the method, system, computing platform, andnon-transient computer readable storage medium may further includeprocesses, features, means, or instructions for analyzing the electricalsignal for the window of time in the frequency domain to identify thetwo amplitude peaks. Some examples of the method, system, computingplatform, and non-transient computer readable storage medium may furtherinclude processes, features, means, or instructions for measuring andanalyzing pressure sensor data during the window of time.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein themachine-learning system is hosted on a local server. In some examples ofthe method, system, computing platform, and non-transientcomputer-readable storage medium described herein the electrical signalis transported via a large area network to a remote machine-learningsystem.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein thedistance from the fracture initiation to the plug is stored with otherdistances from prior fracture initiations to corresponding plugs andreferenced to different plug positions such that a mapping of fractureinitiations in clusters associated with each plug position can bedetermined for a fracking stage.

In some examples, the system further comprises: a wellbore with acasing; and a fracking pump.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described hereinmeasuring a time between the two amplitude peaks comprises measuring atime between a maximum of a first of the two peaks and a maximum of asecond of the two peaks.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein thespeed of sound in the fracking fluid is a function of arrival time of aprimary wave from the fracture initiation and arrival time of areflected wave from the fracture initiation after reflecting off theplug.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein thespeed of sound, S, in the fracking fluid is:

-   -   S=(G+P)+2(F+E)/(A₂−A₁), where G is a firing depth of a        perforation gun, P is a depth of the plug, F is a distance        between the sensor and the wellhead, E is a distance from the        sensor to a fluid end at the surface of the well, A₁ is the        arrival time of the primary wave from the fracture initiation,        and A₂ is the arrival time of the reflected wave from the        fracture initiation and reflected off the plug.

In some examples of the method, system, computing platform, andnon-transient computer-readable storage medium described herein theelectrical signal is transformed from the time domain to the frequencydomain for the window of time, and the two amplitude peaks areidentified by comparing the electrical signal in the frequency domainfor the window of time to frequency domain data associated with previouspairs of amplitude peaks corresponding to previous fracture initiations.

In some examples of the method and non-transient computer-readablestorage medium described herein the measuring is performed by a sensorcoupled to the wellhead, circulating fluid line, or standpipe. In someexamples of the method and non-transient computer-readable storagemedium described herein the measuring is performed by a sensor, andwherein the sensor is configured to be in contact with the frackingfluid in the well or with a surface of the circulating fluid line or thestandpipe at the wellhead.

In some examples of the method and non-transient computer-readablestorage medium described herein, the method further comprisesidentifying the fracture initiation from the electrical signal via amachine-learning system trained on previous frequency domain spectrameasured as a result of previous fracture initiations and previouslyclassified by the machine-learning system.

In some examples of the method and non-transient computer-readablestorage medium described herein the electrical signal is transformedfrom the time domain to the frequency domain for a window of time, andthe two amplitude peaks are identified by comparing the electricalsignal in the frequency domain for the window of time to frequencydomain data associated with previous pairs of amplitude peakscorresponding to previous fracture initiations.

In some examples of the method and non-transient computer-readablestorage medium described herein, the method further comprises performingthe identifying based also on analysis of pressure sensor data duringthe window of time. In some examples of the method and non-transientcomputer-readable storage medium described herein the analyzing isperformed on a locally-hosted machine-learning system. In some examplesof the method and non-transient computer-readable storage mediumdescribed herein the electrical signal in the time domain is transportedvia a large area network to a remote server hosting a machine-learningsystem configured to perform the analyzing.

In some examples of the method and non-transient computer-readablestorage medium described herein, the method further comprises adjustingparameters of subsequent fracking operations to improve clusterefficiency of fracture initiations. In some examples of the method andnon-transient computer-readable storage medium described herein, themethod further comprises storing the distance from the fractureinitiation to the plug with other distances from prior fractureinitiations to the plug, wherein the distance from the fractureinitiation to the plug and the other distances from prior fractureinitiations are referenced to different plug positions such that amapping of fracture initiations in clusters associated with each plugposition can be determined for a fracking stage.

In some examples of the method and non-transient computer-readablestorage medium described herein measuring a time between the twoamplitude peaks comprises measuring a time between a maximum of a firstof the two peaks and a maximum of a second of the two peaks.

In some examples, the speed of sound in the fracking fluid is a functionof arrival time of a primary wave from the fracture initiation andarrival time of a reflected wave from the fracture initiation afterreflecting off the plug, wherein the speed of sound, S, in the frackingfluid is: S=(G+P)+2(F+E)/(A₂−A₁), where G is a firing depth of aperforation gun, P is a depth of the plug, F is a distance between thesensor and the wellhead, E is a distance from the sensor to a fluid endat the surface of the well, A₁ is the arrival time of the primary wavefrom the fracture initiation, and A₂ is the arrival time of thereflected wave from the fracture initiation and reflected off the plug.

In some embodiments, the electrical signal is transformed from the timedomain to the frequency domain for a window of time, and the twoamplitude peaks are identified by comparing the electrical signal in thefrequency domain for the window of time to frequency domain dataassociated with previous pairs of amplitude peaks corresponding toprevious fracture initiations.

In some examples of the method and non-transient computer-readablestorage medium described herein the sensor samples at greater than 1kHz, and the sensor is one of an acoustic or vibration or piezoelectricsensor.

Other aspects of the disclosure can include a non-transitory, tangiblecomputer readable storage medium, encoded with processor readableinstructions to perform a method for determining cluster efficiencyduring hydraulic fracturing operations, the method comprising:converting the acoustic vibrations into an electrical signal in a timedomain; recording the electrical signal to a memory; identifying afracture initiation from the electrical signal in the time domain viaidentification of two amplitude peaks occurring within a thresholdperiod of time of each other; measuring a time between the two amplitudepeaks; dividing the time by two to give a result; multiplying the resultby a speed of sound in the fracking fluid to give a distance between thefracture initiation and a plug at an end of a current fracking stage ofthe well; and using the distance to create a cluster map of fractureinitiations in the stage of the well. The non-transitory, tangiblecomputer readable storage medium can further include the measuring isperformed by a sensor coupled to the wellhead, circulating fluid line,or standpipe. The non-transitory, tangible computer readable storagemedium, wherein the sensor samples at greater than 1 kHz. Thenon-transitory, tangible computer readable storage medium wherein thesensor is an acoustic sensor. The non-transitory, tangible computerreadable storage medium wherein the two amplitude peaks are identifiedbased on exceeding an amplitude threshold. The non-transitory, tangiblecomputer readable storage medium, wherein the two amplitude peaks areidentified based on a combination of amplitude and width. Thenon-transitory, tangible computer readable storage medium, wherein themeasuring is performed by a sensor, and wherein the sensor is configuredto be in contact with the fracking fluid in the well or with a surfaceof the circulating fluid line or the standpipe at the wellhead. Thenon-transitory, tangible computer readable storage medium, wherein themethod further comprises: analyzing the electrical signal for the windowof time in the frequency domain to identify the two amplitude peaks. Thenon-transitory, tangible computer readable storage medium wherein themethod further comprises: performing the identifying based also onanalysis of pressure sensor data during the window of time. Thenon-transitory, tangible computer readable storage medium wherein theanalyzing is performed on a locally-hosted machine-learning system. Thenon-transitory, tangible computer readable storage medium wherein theelectrical signal in the time domain is transported via a large areanetwork to a remote server hosting a machine-learning system configuredto perform the analyzing. The non-transitory, tangible computer readablestorage medium wherein the method further comprises: adjustingparameters of subsequent fracking operations to improve clusterefficiency of fracture initiations. The non-transitory, tangiblecomputer readable storage medium wherein the method further comprises:storing the distance from the fracture initiation to the plug with otherdistances from prior fracture initiations to the plug, and referenced todifferent plug positions such that a mapping of fracture initiations inclusters associated with each plug position can be determined for afracking stage. The non-transitory, tangible computer readable storagemedium wherein, measuring a time between the two amplitude peakscomprises measuring a time between a maximum of a first of the two peaksand a maximum of a second of the two peaks. The non-transitory, tangiblecomputer readable storage medium wherein, the speed of sound in thefracking fluid is a function of arrival time of a primary wave from thefracture initiation and arrival time of a reflected wave from thefracture initiation after reflecting off the plug. The non-transitory,tangible computer readable storage medium wherein, the speed of sound,S, in the fracking fluid is:

S=(G+P)+2(F+E)/(A ₂ −A ₁),

-   -   where G is a firing depth of a perforation gun, P is a depth of        the plug, F is a distance between the sensor and the wellhead, E        is a distance from the sensor to a fluid end at the surface of        the well, A₁ is the arrival time of the primary wave from the        fracture initiation, and A₂ is the arrival time of the reflected        wave from the fracture initiation and reflected off the plug.

The non-transitory, tangible computer readable storage medium wherein,the electrical signal is transformed from the time domain to thefrequency domain for a window of time, and the two amplitude peaks areidentified by comparing the electrical signal in the frequency domainfor the window of time to frequency domain data associated with previouspairs of amplitude peaks corresponding to previous fracture initiations.

Another aspect of the disclosure can be described as a non-transitory,tangible computer readable storage medium, encoded with processorreadable instructions to perform a method of more evenly dispersingfractures within fracture clusters during hydraulic fracturingoperations, the method comprising: pumping fracking fluid into a stageof a well; measuring acoustic vibrations in fracking fluid in awellhead, circulating fluid line, or standpipe of the well; convertingthe acoustic vibrations into an electrical signal in a time domain;recording the electrical signal to a memory; identifying a fractureinitiation from a current frequency domain spectrum via amachine-learning system trained on previous frequency domain spectrameasured as a result of previous fracture initiations and previouslyclassified by the machine-learning system; analyzing the electricalsignal in the time domain during a window of time and identifying twoamplitude peaks corresponding to the fracture initiation found in thecurrent frequency domain spectrum; measuring a time between the twoamplitude peaks; dividing the time by two to give a result; multiplyingthe result by a speed of sound in fracking fluid thought to be near thefracture initiation to give a distance from the fracture initiation to aplug; and using the distance to create a cluster map of fractureinitiations in the stage of the well; and adjusting a parameter of thehydraulic fracturing operations based on the cluster map to achieve moreeven dispersion of fractures in a subsequent stage. The non-transitory,tangible computer readable storage medium wherein, the measuring isperformed by a sensor coupled to the wellhead, the circulating fluidline, or the standpipe. The non-transitory, tangible computer readablestorage medium wherein, the sensor samples at greater than 1 kHz. Thenon-transitory, tangible computer readable storage medium wherein, thesensor is an acoustic sensor. The non-transitory, tangible computerreadable storage medium wherein, the two amplitude peaks are identifiedbased on exceeding an amplitude threshold. The non-transitory, tangiblecomputer readable storage medium wherein, the two amplitude peaks areidentified based on a combination of amplitude and width. Thenon-transitory, tangible computer readable storage medium wherein, thetwo amplitude peaks are identified based on a frequency of the twoamplitude peaks. The non-transitory, tangible computer readable storagemedium wherein, the measuring is performed by a sensor, and wherein thesensor is configured to be in contact with the fracking fluid in thewell or with a surface of the circulating fluid line or a surface of thestandpipe at the wellhead. The non-transitory, tangible computerreadable storage medium wherein, the method further comprises:performing the identifying based also on analysis of pressure sensordata during the window of time. The non-transitory, tangible computerreadable storage medium wherein, the analyzing is performed on alocally-hosted machine-learning system. The non-transitory, tangiblecomputer readable storage medium wherein, the electrical signal in thetime domain is transported via a large area network to a remote serverhosting a machine-learning system configured to perform the analyzing.The non-transitory, tangible computer readable storage medium wherein,the method further comprises storing the distance from the fractureinitiation to the plug with other distances from prior fractureinitiations to the plug, and referenced to different plug positions suchthat a mapping of fracture initiations in clusters associated with eachplug position can be determined for a fracking stage. Thenon-transitory, tangible computer readable storage medium wherein,measuring a time between the two amplitude peaks comprises measuring atime between a maximum of a first of the two peaks and a maximum of asecond of the two peaks. The non-transitory, tangible computer readablestorage medium wherein, the speed of sound in the fracking fluid is afunction of arrival time of a primary wave from the fracture initiationand arrival time of a reflected wave from the fracture initiation afterreflecting off the plug. The non-transitory, tangible computer readablestorage medium wherein, the speed of sound, S, in the fracking fluid is:

S=(G+P)+2(F+E)/(A ₂ −A ₁),

-   -   where G is a firing depth of a perforation gun, P is a depth of        the plug, F is a distance between the sensor and the wellhead, E        is a distance from the sensor to a fluid end at the surface of        the well, A₁ is the arrival time of the primary wave from the        fracture initiation, and A₂ is the arrival time of the reflected        wave from the fracture initiation and reflected off the plug.

Some portions are presented in terms of algorithms or symbolicrepresentations of operations on data bits or binary digital signalsstored within a computing system memory, such as a computer memory.These algorithmic descriptions or representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Analgorithm is a self-consistent sequence of operations or similarprocessing leading to a desired result. In this context, operations orprocessing involves physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese and similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, it is appreciated that throughout this specificationdiscussions utilizing terms such as “processing,” “computing,”“calculating,” “determining,” and “identifying” or the like refer toactions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulate or transform data represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of thecomputing platform.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

As used herein, the recitation of “at least one of A, B and C” isintended to mean “either A, B, C or any combination of A, B and C.” Theprevious description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the present disclosure.Various modifications to these embodiments will be readily apparent tothose skilled in the art, and the generic principles defined herein maybe applied to other embodiments without departing from the spirit orscope of the disclosure. Thus, the present disclosure is not intended tobe limited to the embodiments shown herein but is to be accorded thewidest scope consistent with the principles and novel features disclosedherein.

What is claimed is:
 1. A non-transitory, tangible computer readablestorage medium, encoded with processor readable instructions to performa method for determining cluster efficiency during hydraulic fracturingoperations, the method comprising: converting the acoustic vibrationsinto an electrical signal in a time domain; recording the electricalsignal to a memory; identifying a fracture initiation from theelectrical signal in the time domain via identification of two amplitudepeaks occurring within a threshold period of time of each other;measuring a time between the two amplitude peaks; dividing the time bytwo to give a result; multiplying the result by a speed of sound in thefracking fluid to give a distance between the fracture initiation and aplug at an end of a current fracking stage of the well; and using thedistance to create a cluster map of fracture initiations in the stage ofthe well.
 2. The non-transitory, tangible computer readable storagemedium of claim 1, wherein the measuring is performed by a sensorcoupled to the wellhead, circulating fluid line, or standpipe.
 3. Thenon-transitory, tangible computer readable storage medium of claim 2,wherein the sensor samples at greater than 1 kHz.
 4. The non-transitory,tangible computer readable storage medium of claim 2, wherein the sensoris an acoustic sensor.
 5. The non-transitory, tangible computer readablestorage medium of claim 1, wherein the two amplitude peaks areidentified based on exceeding an amplitude threshold.
 6. Thenon-transitory, tangible computer readable storage medium of claim 1,wherein the two amplitude peaks are identified based on a combination ofamplitude and width.
 7. The non-transitory, tangible computer readablestorage medium of claim 1, wherein the measuring is performed by asensor, and wherein the sensor is configured to be in contact with thefracking fluid in the well or with a surface of the circulating fluidline or the standpipe at the wellhead.
 8. The non-transitory, tangiblecomputer readable storage medium of claim 1, wherein the method furthercomprises: analyzing the electrical signal for the window of time in thefrequency domain to identify the two amplitude peaks.
 9. Thenon-transitory, tangible computer readable storage medium of claim 1,wherein the method further comprises: performing the identifying basedalso on analysis of pressure sensor data during the window of time. 10.The non-transitory, tangible computer readable storage medium of claim1, wherein the analyzing is performed on a locally-hostedmachine-learning system.
 11. The non-transitory, tangible computerreadable storage medium of claim 1, wherein the electrical signal in thetime domain is transported via a large area network to a remote serverhosting a machine-learning system configured to perform the analyzing.12. The non-transitory, tangible computer readable storage medium ofclaim 1, wherein the method further comprises: adjusting parameters ofsubsequent fracking operations to improve cluster efficiency of fractureinitiations.
 13. The non-transitory, tangible computer readable storagemedium of claim 1, wherein the method further comprises: storing thedistance from the fracture initiation to the plug with other distancesfrom prior fracture initiations to the plug, and referenced to differentplug positions such that a mapping of fracture initiations in clustersassociated with each plug position can be determined for a frackingstage.
 14. The non-transitory, tangible computer readable storage mediumof claim 1, wherein measuring a time between the two amplitude peakscomprises measuring a time between a maximum of a first of the two peaksand a maximum of a second of the two peaks.
 15. The non-transitory,tangible computer readable storage medium of claim 1, wherein the speedof sound in the fracking fluid is a function of arrival time of aprimary wave from the fracture initiation and arrival time of areflected wave from the fracture initiation after reflecting off theplug.
 16. The non-transitory, tangible computer readable storage mediumof claim 15, wherein the speed of sound, S, in the fracking fluid is:S=(G+P)+2(F+E)/(A ₂ −A ₁), where G is a firing depth of a perforationgun, P is a depth of the plug, F is a distance between the sensor andthe wellhead, E is a distance from the sensor to a fluid end at thesurface of the well, A₁ is the arrival time of the primary wave from thefracture initiation, and A₂ is the arrival time of the reflected wavefrom the fracture initiation and reflected off the plug.
 17. Thenon-transitory, tangible computer readable storage medium of claim 1,wherein the electrical signal is transformed from the time domain to thefrequency domain for a window of time, and the two amplitude peaks areidentified by comparing the electrical signal in the frequency domainfor the window of time to frequency domain data associated with previouspairs of amplitude peaks corresponding to previous fracture initiations.18. A non-transitory, tangible computer readable storage medium, encodedwith processor readable instructions to perform a method of more evenlydispersing fractures within fracture clusters during hydraulicfracturing operations, the method comprising: pumping fracking fluidinto a stage of a well; measuring acoustic vibrations in fracking fluidin a wellhead, circulating fluid line, or standpipe of the well;converting the acoustic vibrations into an electrical signal in a timedomain; recording the electrical signal to a memory; identifying afracture initiation from a current frequency domain spectrum via amachine-learning system trained on previous frequency domain spectrameasured as a result of previous fracture initiations and previouslyclassified by the machine-learning system; analyzing the electricalsignal in the time domain during a window of time and identifying twoamplitude peaks corresponding to the fracture initiation found in thecurrent frequency domain spectrum; measuring a time between the twoamplitude peaks; dividing the time by two to give a result; multiplyingthe result by a speed of sound in fracking fluid thought to be near thefracture initiation to give a distance from the fracture initiation to aplug; and using the distance to create a cluster map of fractureinitiations in the stage of the well; and adjusting a parameter of thehydraulic fracturing operations based on the cluster map to achieve moreeven dispersion of fractures in a subsequent stage.
 19. Thenon-transitory, tangible computer readable storage medium of claim 18,wherein the measuring is performed by a sensor coupled to the wellhead,the circulating fluid line, or the standpipe.
 20. The non-transitory,tangible computer readable storage medium of claim 19, wherein thesensor samples at greater than 1 kHz.
 21. The non-transitory, tangiblecomputer readable storage medium of claim 19, wherein the sensor is anacoustic sensor.
 22. The non-transitory, tangible computer readablestorage medium of claim 18, wherein the two amplitude peaks areidentified based on exceeding an amplitude threshold.
 23. Thenon-transitory, tangible computer readable storage medium of claim 18,wherein the two amplitude peaks are identified based on a combination ofamplitude and width.
 24. The non-transitory, tangible computer readablestorage medium of claim 18, wherein the two amplitude peaks areidentified based on a frequency of the two amplitude peaks.
 25. Thenon-transitory, tangible computer readable storage medium of claim 18,wherein the measuring is performed by a sensor, and wherein the sensoris configured to be in contact with the fracking fluid in the well orwith a surface of the circulating fluid line or a surface of thestandpipe at the wellhead.
 26. The non-transitory, tangible computerreadable storage medium of claim 18, wherein the method furthercomprises: performing the identifying based also on analysis of pressuresensor data during the window of time.
 27. The non-transitory, tangiblecomputer readable storage medium of claim 18, wherein the analyzing isperformed on a locally-hosted machine-learning system.
 28. Thenon-transitory, tangible computer readable storage medium of claim 18,wherein the electrical signal in the time domain is transported via alarge area network to a remote server hosting a machine-learning systemconfigured to perform the analyzing.